CN110135517A - For obtaining the method and device of vehicle similarity - Google Patents

For obtaining the method and device of vehicle similarity Download PDF

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Publication number
CN110135517A
CN110135517A CN201910441616.5A CN201910441616A CN110135517A CN 110135517 A CN110135517 A CN 110135517A CN 201910441616 A CN201910441616 A CN 201910441616A CN 110135517 A CN110135517 A CN 110135517A
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vehicle
sample
similarity
information
image
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CN110135517B (en
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段旭
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Beijing Baidu Netcom Science and Technology Co Ltd
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Beijing Baidu Netcom Science and Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/758—Involving statistics of pixels or of feature values, e.g. histogram matching

Abstract

Embodiment of the disclosure discloses the method and device for obtaining vehicle similarity.One specific embodiment of this method includes: to choose first area and second area from the first image to be processed and the second image to be processed；The first vehicle characteristic information is obtained from first area and obtains the second vehicle characteristic information from second area；According to the similarity of the first vehicle characteristic information of first area and the second vehicle characteristic information of corresponding second area, the first vehicle similarity information is obtained；The third vehicle characteristic information and the 4th vehicle characteristic information of the first image to be processed and the second image to be processed are obtained respectively；Third vehicle characteristic information and the 4th vehicle characteristic information are imported into vehicle identification model, obtain the second vehicle similarity information；According to the first vehicle similarity information and the second vehicle similarity information, the similarity between the corresponding vehicle of the first vehicle image and the corresponding vehicle of the second vehicle image is calculated.This embodiment improves the accuracys to vehicle identification.

Description

For obtaining the method and device of vehicle similarity
Technical field
Embodiment of the disclosure is related to technical field of image processing, and in particular to for obtain vehicle similarity method and Device.
Background technique
With the rapid development of modern social economy, effective urban traffic management is living in the economy of people, society Importance in dynamic is increasingly significant.Therefore, further investigation, which solves urban transport problems, just particularly important realistic meaning.Its In, effective identification to vehicle is the core of modern intelligent transportation research.
Summary of the invention
Embodiment of the disclosure proposes the method and device for obtaining vehicle similarity.
In a first aspect, embodiment of the disclosure provides a kind of method for obtaining vehicle similarity, this method comprises: Multiple first areas and multiple second areas are chosen from the first image to be processed and the second image to be processed respectively, wherein described First image to be processed includes the first vehicle image, and the second image to be processed includes the second vehicle image, the multiple firstth area Vehicle location in vehicle location and the multiple second area in domain in each first area in each second area is corresponding； From each of the multiple first area first area obtain the first vehicle characteristic information respectively and from the multiple second Each of region second area obtains the second vehicle characteristic information；It is special according to the first vehicle of each first area Reference ceases and the similarity of the second vehicle characteristic information of corresponding each second area, the first vehicle similarity of acquisition Information；Third vehicle characteristic information and the 4th vehicle for obtaining the described first image to be processed and the second image to be processed respectively are special Reference breath；The third vehicle characteristic information and the 4th vehicle characteristic information are imported into vehicle identification model trained in advance, obtained To the second vehicle similarity information；According to the first vehicle similarity information and the second vehicle similarity information, calculate Similarity between the corresponding vehicle of first vehicle image and the corresponding vehicle of the second vehicle image.
In some embodiments, the first vehicle characteristic information of each first area according to and corresponding institute The similarity of the second vehicle characteristic information of each second area is stated, obtains the first vehicle similarity information, comprising: for more A first vehicle characteristic information and the first vehicle characteristic information and the second vehicle characteristics letter in multiple second vehicle characteristic informations Breath, the son calculated between the first vehicle characteristic information and the second vehicle characteristic information of corresponding first vehicle characteristic information are similar Spend information；Using the sum of multiple sub- similarity informations as the first vehicle similarity information.
In some embodiments, above-mentioned vehicle identification model is obtained by following steps training: obtaining multiple sample vehicles Image group first sample vehicle characteristics corresponding with sample vehicle image group each in corresponding above-mentioned multiple sample vehicle image groups Information, the second sample vehicle characteristic information and Sample Similarity information, wherein sample vehicle image group includes comprising vehicle image First sample vehicle subgraph and the second sample vehicle subgraph comprising vehicle image, first sample vehicle subgraph and One sample vehicle characteristic information is corresponding, the second sample vehicle subgraph and the second sample vehicle characteristic information, Sample Similarity letter Breath is for characterizing the similarity between first sample vehicle subgraph and the second sample vehicle subgraph, sample vehicle characteristic information Include at least one of the following: vehicle wheel profile, vehicle region line；By each sample vehicle of above-mentioned multiple sample vehicle image groups The first sample vehicle characteristic information of image group and the second sample vehicle characteristic information are as input, by above-mentioned multiple sample vehicles Above-mentioned Sample Similarity information corresponding to each sample vehicle image group in image group obtains above-mentioned vehicle as output, training Identification model.
In some embodiments, the first of above-mentioned each sample vehicle image group by above-mentioned multiple sample vehicle image groups Sample vehicle characteristic information and the second sample vehicle characteristic information, will be every in above-mentioned multiple sample vehicle image groups as input Above-mentioned Sample Similarity information corresponding to a sample vehicle image group obtains above-mentioned vehicle identification model as output, training, It include: to execute following training step: by the first sample of each sample vehicle image group in above-mentioned multiple sample vehicle image groups This vehicle characteristic information and the second sample vehicle characteristic information are sequentially input to initial vehicle identification model, obtain above-mentioned multiple samples Prediction similarity information corresponding to each sample vehicle image group in this vehicle image group, by above-mentioned multiple sample vehicle figures Sample corresponding to prediction similarity information and the sample vehicle image group as corresponding to each sample vehicle image group in group This similarity information is compared, and is obtained the predictablity rate of above-mentioned initial vehicle identification model, is determined above-mentioned predictablity rate Whether be greater than default accuracy rate threshold value, if more than above-mentioned default accuracy rate threshold value, then using above-mentioned initial vehicle identification model as The vehicle identification model that training is completed.
In some embodiments, the first of above-mentioned each sample vehicle image group by above-mentioned multiple sample vehicle image groups Sample vehicle characteristic information and the second sample vehicle characteristic information, will be every in above-mentioned multiple sample vehicle image groups as input Above-mentioned Sample Similarity information corresponding to a sample vehicle image group obtains above-mentioned vehicle identification model as output, training, It include: in response to adjusting the parameter of above-mentioned initial vehicle identification model, and continue to execute no more than above-mentioned default accuracy rate threshold value Above-mentioned training step.
In some embodiments, above-mentioned Sample Similarity information is obtained by following steps: according to sample vehicle image group The first sample vehicle subgraph and the corresponding first sample vehicle characteristic information of the second sample vehicle subgraph that include and Second sample vehicle characteristic information determines the second vehicle region of the first vehicle region of sample and sample；Above-mentioned sample is obtained respectively The first area feature and second area feature of the first vehicle region of sample of the second vehicle region of one vehicle region and sample, In, provincial characteristics includes at least one of the following: lines assemblage characteristic, region assemblage characteristic；Respectively to above-mentioned first area feature Fusion Features are carried out with second area feature, obtain first area characteristic value and second area characteristic value；According to above-mentioned firstth area Difference between characteristic of field value and second area characteristic value calculates Sample Similarity information.
In some embodiments, described to be believed according to the first vehicle similarity information and the second vehicle similarity Breath calculates the similarity between the corresponding vehicle of first vehicle image and the corresponding vehicle of the second vehicle image, comprising: point It Wei not the first vehicle similarity information and the second vehicle similarity information the first weight of setting and the second weight；By institute The first product for stating the first weight and the first vehicle similarity information, second with the second weight and the second vehicle similarity information Sum between product is as the similarity between vehicle.
Second aspect, embodiment of the disclosure provide a kind of for obtaining the device of vehicle similarity, which includes: Image-region selecting unit is configured to choose multiple first areas from the first image to be processed and the second image to be processed respectively With multiple second areas, wherein first image to be processed includes the first vehicle image, and the second image to be processed includes second Vehicle image, each in the vehicle location and the multiple second area in the multiple first area in each first area Vehicle location in two regions is corresponding；Fisrt feature information acquisition unit is configured to respectively from the multiple first area Each first area obtain and the first vehicle characteristic information and obtained from each of the multiple second area second area Take the second vehicle characteristic information；First vehicle similarity information acquiring unit is configured to according to each described first area The first vehicle characteristic information and the similarity of the second vehicle characteristic information of corresponding each second area, obtain the One vehicle similarity information；Second feature information acquisition unit is configured to obtain the described first image to be processed and respectively The third vehicle characteristic information and the 4th vehicle characteristic information of two images to be processed；Second vehicle similarity information acquiring unit, It is configured to the third vehicle characteristic information and the 4th vehicle characteristic information importing vehicle identification model trained in advance, obtain To the second vehicle similarity information；Similarity calculated is configured to according to the first vehicle similarity information and described Second vehicle similarity information calculates between the corresponding vehicle of first vehicle image and the corresponding vehicle of the second vehicle image Similarity.
In some embodiments, the first vehicle similarity information acquiring unit includes: that sub- similarity information calculates son Unit, for the first vehicle characteristic information and second in multiple first vehicle characteristic informations and multiple second vehicle characteristic informations Vehicle characteristic information, the second vehicle for being configured to calculate the first vehicle characteristic information and corresponding first vehicle characteristic information are special Sub- similarity information between reference breath；First vehicle similarity information obtains subelement, is configured to multiple sub- similarities The sum of information is as the first vehicle similarity information.
In some embodiments, above-mentioned apparatus further includes vehicle identification model training unit, is configured to that vehicle is trained to know Other model, above-mentioned vehicle identification model training unit include: that sample information obtains subelement, are configured to obtain multiple sample vehicles Image group first sample vehicle corresponding with sample vehicle image group each in corresponding above-mentioned multiple sample vehicle image groups is special Reference breath, the second sample vehicle characteristic information and Sample Similarity information, wherein sample vehicle image group includes comprising vehicle figure The first sample vehicle subgraph of picture and the second sample vehicle subgraph comprising vehicle image, first sample vehicle subgraph with First sample vehicle characteristic information is corresponding, the second sample vehicle subgraph and the second sample vehicle characteristic information, Sample Similarity Information is used to characterize the similarity between first sample vehicle subgraph and the second sample vehicle subgraph, sample vehicle characteristics letter Breath includes at least one of the following: vehicle wheel profile, vehicle region line；Vehicle identification model training subelement, being configured to will be upper State the first sample vehicle characteristic information and the second sample vehicle of each sample vehicle image group of multiple sample vehicle image groups Characteristic information is as input, by above-mentioned sample corresponding to each sample vehicle image group in above-mentioned multiple sample vehicle image groups This similarity information obtains above-mentioned vehicle identification model as output, training.
In some embodiments, above-mentioned vehicle identification model training subelement includes: vehicle identification model training module, quilt Be configured to the first sample vehicle characteristic information of each sample vehicle image group in above-mentioned multiple sample vehicle image groups and Second sample vehicle characteristic information is sequentially input to initial vehicle identification model, is obtained in above-mentioned multiple sample vehicle image groups Prediction similarity information corresponding to each sample vehicle image group, by each sample in above-mentioned multiple sample vehicle image groups Sample Similarity information corresponding to prediction similarity information and the sample vehicle image group corresponding to vehicle image group carries out Compare, obtain the predictablity rate of above-mentioned initial vehicle identification model, it is default accurate to determine whether above-mentioned predictablity rate is greater than Rate threshold value then knows the vehicle that above-mentioned initial vehicle identification model is completed as training if more than above-mentioned default accuracy rate threshold value Other model.
In some embodiments, above-mentioned vehicle identification model training subelement includes: parameter adjustment module, in response to little It in above-mentioned default accuracy rate threshold value, is configured to adjust the parameter of above-mentioned initial vehicle identification model, and returns to vehicle identification mould Type training module.
In some embodiments, above-mentioned apparatus further includes Sample Similarity information acquisition unit, is configured to obtain sample Similarity information, above-mentioned Sample Similarity information acquisition unit include: that sample vehicle region obtains subelement, are configured to basis The first sample vehicle subgraph and the corresponding first sample of the second sample vehicle subgraph that sample vehicle image group includes Vehicle characteristic information and the second sample vehicle characteristic information determine the second vehicle region of the first vehicle region of sample and sample；Region Feature obtains subelement, is configured to obtain the sample the of above-mentioned the first vehicle region of sample and the second vehicle region of sample respectively The first area feature and second area feature of one vehicle region, wherein provincial characteristics includes at least one of the following: that lines combine Feature, region assemblage characteristic；Regional characteristic value obtains subelement, is configured to respectively to above-mentioned first area feature and the secondth area Characteristic of field carries out Fusion Features, obtains first area characteristic value and second area characteristic value；It is single that Sample Similarity information calculates son Member is configured to calculate Sample Similarity letter according to the difference between above-mentioned first area characteristic value and second area characteristic value Breath.
In some embodiments, the similarity calculated includes: weight setting subelement, is configured to respectively institute It states the first vehicle similarity information and the first weight and the second weight is arranged in the second vehicle similarity information；Similarity calculation Subelement is configured to the first product of first weight and the first vehicle similarity information, with the second weight and second Sum between second product of vehicle similarity information is as the similarity between vehicle.
The third aspect, embodiment of the disclosure provide a kind of electronic equipment, comprising: one or more processors；Storage Device is stored thereon with one or more programs, when said one or multiple programs are executed by said one or multiple processors, So that said one or multiple processors execute the method for obtaining vehicle similarity of above-mentioned first aspect.
Fourth aspect, embodiment of the disclosure provide a kind of computer-readable medium, are stored thereon with computer program, It is characterized in that, the program realizes the method for obtaining vehicle similarity of above-mentioned first aspect when being executed by processor.
Embodiment of the disclosure provide for obtaining the method and device of vehicle similarity, firstly, respectively from first to It handles image and the second image to be processed chooses multiple first areas and multiple second areas, respectively from the multiple first area Each of first area obtain the first vehicle characteristic information and from each of the multiple second area second area Obtain the second vehicle characteristic information, according to the first vehicle characteristic information of each first area with it is corresponding described each The similarity of second vehicle characteristic information of a second area obtains the first vehicle similarity information；Then, described in obtaining respectively The third vehicle characteristic information and the 4th vehicle characteristic information of first image to be processed and the second image to be processed, by the third Vehicle characteristic information and the 4th vehicle characteristic information import vehicle identification model trained in advance, obtain the second vehicle similarity letter Breath；Finally, calculating the first vehicle figure according to the first vehicle similarity information and the second vehicle similarity information As the similarity between corresponding vehicle and the corresponding vehicle of the second vehicle image.The technical solution of the application is improved to vehicle The accuracy of identification.
Detailed description of the invention
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, the disclosure is other Feature, objects and advantages will become more apparent upon:
Fig. 1 is that one embodiment of the disclosure can be applied to exemplary system architecture figure therein；
Fig. 2 is the flow chart according to one embodiment of the method for obtaining vehicle similarity of the disclosure；
Fig. 3 a is the first image to be processed of an application scenarios of the disclosure；
Fig. 3 b is the second image to be processed of the image to be processed of corresponding diagram 3a first；
Fig. 4 is the flow chart according to one embodiment of the vehicle identification model training method of the disclosure；
Fig. 5 is the structural schematic diagram according to one embodiment of the device for obtaining vehicle similarity of the disclosure；
Fig. 6 is adapted for the electronic devices structure schematic diagram for realizing embodiment of the disclosure.
Specific embodiment
The disclosure is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched The specific embodiment stated is used only for explaining related invention, rather than the restriction to the invention.It also should be noted that in order to Convenient for description, part relevant to related invention is illustrated only in attached drawing.
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the disclosure can phase Mutually combination.The disclosure is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
Fig. 1 is shown can be using embodiment of the disclosure for obtaining the method for vehicle similarity or for obtaining vehicle The exemplary system architecture 100 of the device of similarity.
As shown in Figure 1, system architecture 100 may include image collecting device 101, network 102 and server 103.Network 102 between vehicle 101 and server 103 to provide the medium of communication link.Network 102 may include various connection classes Type, such as wired, wireless communication link or fiber optic cables etc..
Image collecting device 101 is interacted by network 102 with server 103, to receive or send message etc..Image Acquisition Various image processing applications can be installed, such as Image Acquisition application, vehicle identification are applied, vehicle match is answered on device 101 With, marking of cars application etc..
Image collecting device 101 can be hardware, be also possible to software.It, can be with when image collecting device 101 is hardware The various electronic equipments that there is camera and support image procossing, including but not limited to intelligent video camera head, vehicle-mounted camera, Monitoring camera head etc..When image collecting device 101 is software, may be mounted in above-mentioned cited electronic equipment.It can To be implemented as multiple softwares or software module (such as providing Distributed Services), single software or software also may be implemented into Module is not specifically limited herein.
Server 103 can be to provide the server of various services, such as send to image collecting device 101 to be processed The server of image progress image procossing.Server can carry out the processing such as analyzing to data such as the images to be processed received, To obtain corresponding processing result (such as similarity between vehicle).
It should be noted that can be by image for obtaining the method for vehicle similarity provided by embodiment of the disclosure Acquisition device 101 is individually performed, or can also be executed jointly by image collecting device 101 and server 103.Correspondingly, it uses It can be set in image collecting device 101, also can be set in server 103 in the device for obtaining vehicle similarity.
It should be noted that server can be hardware, it is also possible to software.When server is hardware, may be implemented At the distributed server cluster that multiple servers form, individual server also may be implemented into.It, can when server is software To be implemented as multiple softwares or software module (such as providing Distributed Services), single software or software also may be implemented into Module is not specifically limited herein.
It should be understood that the number of image collecting device, network and server in Fig. 1 is only schematical.According to reality It now needs, can have any number of image collecting device, network and server.
With continued reference to Fig. 2, one embodiment of the method for obtaining vehicle similarity according to the disclosure is shown Process 200.This be used for obtain vehicle similarity method the following steps are included:
Step 201, multiple first areas and multiple the are chosen from the first image to be processed and the second image to be processed respectively Two regions.
In the present embodiment, for obtaining executing subject (such as the Image Acquisition shown in FIG. 1 of the method for vehicle similarity Device 101 and/or server 103) the first image to be processed can be obtained by wired connection mode or radio connection With the second image to be processed.Wherein, the described first image to be processed includes the first vehicle image, and the second image to be processed includes the Two vehicle images, it is each in the vehicle location and the multiple second area in the multiple first area in each first area Vehicle location in second area is corresponding.It should be pointed out that above-mentioned radio connection can include but is not limited to 3G/4G company It connects, WiFi connection, bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection and other are existing In known or exploitation in the future radio connection.
In order to compare multiple first vehicle images that the first image to be processed includes and that the second image to be processed includes is more Similarity between a second vehicle image determines that the corresponding real vehicles of the first vehicle image and the second vehicle image are corresponding Whether real vehicles are same vehicle (or same type of vehicles), and executing subject can be respectively from the first image to be processed and Two images to be processed choose multiple first areas and multiple second areas.Wherein, the vehicle location in first area and the secondth area Vehicle location in domain is corresponding.For example, being the first vehicle image corresponding first tailstock position in first area, in second area For the second vehicle image, with the first tailstock position corresponding second tailstock position.
Step 202, the first vehicle characteristic information is obtained from each of the multiple first area first area respectively The second vehicle characteristic information is obtained with from each of the multiple second area second area.
After choosing first area and second area, executing subject can obtain first from first area and second area respectively Vehicle characteristic information and the second vehicle characteristic information.Wherein, the first vehicle characteristic information (or second vehicle characteristic information) can be with It is the information such as color characteristic information, texture feature information, vehicle structure characteristic information.It should be noted that the first vehicle characteristics Information and the second vehicle characteristic information be it is corresponding, i.e. when the first vehicle characteristic information is color characteristic information, the second vehicle is special Reference breath is also color characteristic information, carries out phase will pass through same characteristic information to the first vehicle image and the second vehicle image Judge like degree.First vehicle characteristic information and the second vehicle characteristic information can characterize the first image to be processed and second to be processed The local feature of image.
Step 203, according to the first vehicle characteristic information of each first area with it is corresponding it is described each The similarity of second vehicle characteristic information in two regions obtains the first vehicle similarity information.
In general, the first vehicle similarity information is identified by way of decimal.For example, the first vehicle similarity information can be with It is 0.568 etc..First vehicle similarity information may is that high similarity, middle similarity, low similarity；It may also is that 100% Similarity, 30% similarity etc.；It may also is that vehicle similar with color, vehicle Similar color dissmilarity, vehicle and color not It is similar etc..According to the actual situation, similarity information can also be other kinds of describing mode.
Executing subject can calculate in several ways first area the first vehicle characteristic information and corresponding secondth area Similarity between second vehicle characteristic information in domain obtains the first vehicle similarity information.
In some optional implementations of the present embodiment, the first vehicle of each first area according to It is similar to obtain the first vehicle for the similarity of characteristic information and the second vehicle characteristic information of corresponding each second area Information is spent, may comprise steps of:
The first step, for the first vehicle characteristics in multiple first vehicle characteristic informations and multiple second vehicle characteristic informations Information and the second vehicle characteristic information calculate the second vehicle of the first vehicle characteristic information and corresponding first vehicle characteristic information Sub- similarity information between characteristic information.
First vehicle characteristic information and the second vehicle characteristic information can be converted into corresponding fisrt feature by executing subject Vector sum second feature vector.Then the COS distance between first eigenvector and second feature vector is calculated, sub- phase is obtained Like degree information.
Second step, using the sum of multiple sub- similarity informations as the first vehicle similarity information.
Later, executing subject can calculate multiple first vehicle characteristic informations and multiple second vehicle characteristic informations are corresponding The sum of multiple sub- similarity informations, and the result that sub- similarity information is summed is as the first vehicle similarity information.
Step 204, the third vehicle characteristic information of the described first image to be processed and the second image to be processed is obtained respectively With the 4th vehicle characteristic information.
Executing subject can carry out the spy of (or globality) of overall importance to the first image to be processed and the second image to be processed Sign is extracted, and third vehicle characteristic information and the 4th vehicle characteristic information are obtained.
Step 205, the third vehicle characteristic information and the 4th vehicle characteristic information vehicle trained in advance is imported to know Other model obtains the second vehicle similarity information.
After obtaining third vehicle characteristic information and the 4th vehicle characteristic information, executing subject can believe third vehicle characteristics Breath and the 4th vehicle characteristic information import vehicle identification model trained in advance, obtain the second vehicle similarity information.For example, holding Row main body can constitute global characteristics vector by third vehicle characteristic information and the 4th vehicle characteristic information, then utilize joint The models such as bayesian algorithm model obtain the second vehicle similarity information (i.e. global similarity).Wherein, above-mentioned vehicle identification mould Type can be used for calculating by third vehicle characteristic information and the 4th vehicle characteristic information the corresponding vehicle of the first vehicle image and Similarity information between the corresponding vehicle of second vehicle image.Second vehicle similarity information and the first vehicle similarity information It is identical.
In some optional implementations of the present embodiment, above-mentioned vehicle identification model is trained by following steps It arrives:
The first step obtains each sample vehicle in multiple sample vehicle image groups and corresponding above-mentioned multiple sample vehicle image groups The corresponding first sample vehicle characteristic information of image group, the second sample vehicle characteristic information and Sample Similarity information.
In order to train vehicle identification model, executing subject can obtain multiple sample vehicle image groups first and correspondence is above-mentioned The corresponding first sample vehicle characteristic information of each sample vehicle image group, the second sample vehicle in multiple sample vehicle image groups Characteristic information and Sample Similarity information.Wherein, sample vehicle image group may include first sample vehicle subgraph and second Sample vehicle subgraph.First sample vehicle sub-picture pack contains vehicle image, and the second sample vehicle subgraph also includes vehicle figure Picture.First sample vehicle subgraph is corresponding with first sample vehicle characteristic information, the second sample vehicle subgraph and the second sample Vehicle characteristic information.That is, first sample vehicle characteristic information is to carry out feature extraction to first sample vehicle subgraph to obtain； Second sample vehicle characteristic information is to carry out feature extraction to the second sample vehicle subgraph to obtain.Sample Similarity information can For characterizing the similarity between first sample vehicle subgraph and the second sample vehicle subgraph, sample vehicle characteristic information It may include at least one of following: vehicle wheel profile, vehicle region line.
Second step, by the first sample vehicle characteristics of each sample vehicle image group of above-mentioned multiple sample vehicle image groups Information and the second sample vehicle characteristic information are as input, by each sample vehicle figure in above-mentioned multiple sample vehicle image groups It is exported as the corresponding above-mentioned Sample Similarity information of group is used as, training obtains above-mentioned vehicle identification model.
After getting sample vehicle image group, executing subject can be by each sample of above-mentioned multiple sample vehicle image groups The first sample vehicle characteristic information of vehicle image group and the second sample vehicle characteristic information are as input, by above-mentioned multiple samples Above-mentioned Sample Similarity information corresponding to each sample vehicle image group in vehicle image group is obtained as output, training State vehicle identification model.
In some optional implementations of the present embodiment, above-mentioned Sample Similarity information is obtained by following steps:
The first step, the first sample vehicle subgraph for including according to sample vehicle image group and the second sample vehicle subgraph Corresponding first sample vehicle characteristic information and the second sample vehicle characteristic information determine the first vehicle region of sample and sample This second vehicle region.
Seen from the above description, the first sample vehicle subgraph and the second sample vehicle that each sample vehicle image group includes Subgraph.Executing subject can be respectively to first sample vehicle subgraph and corresponding first sample of the second sample vehicle subgraph This vehicle characteristic information and the second sample vehicle characteristic information carry out data processing, determine the first vehicle region of sample and sample the Two vehicle regions.For example, executing subject can be to the vehicle wheel profile and/or vehicle area that first sample vehicle characteristic information includes Domain line is extended, and then carries out vehicle region division to vehicle, determines the first vehicle region of sample.Wherein, vehicle region can To include at least one following region: the regions such as headstock region, tailstock region, car door region, car roof area.Vehicle region may be used also Be vehicle multiple regions juncture area, such as can be the juncture area etc. of tailstock region and car roof area.Execute master Body can carry out identical processing to the second sample vehicle characteristic information, obtain the second vehicle region of sample.
Second step obtains the first vehicle of sample area of above-mentioned the first vehicle region of sample and the second vehicle region of sample respectively The first area feature and second area feature in domain.
Later, executing subject can respectively propose the first vehicle region of sample and the second vehicle region of sample progress feature It takes, the second area of the first area feature and corresponding the second vehicle region of sample that obtain corresponding the first vehicle region of sample is special Sign.Wherein, provincial characteristics includes at least one of the following: the provincial characteristics such as lines assemblage characteristic, region assemblage characteristic.
Third step carries out Fusion Features to above-mentioned first area feature and second area feature respectively, obtains first area Characteristic value and second area characteristic value.
Executing subject can carry out Fusion Features to above-mentioned first area feature and second area feature respectively, obtain first Regional characteristic value and second area characteristic value.For example, executing subject can be according to the corresponding vehicle area of the first vehicle region of sample Correlation (such as can be positional relationship, color-match relationship etc.) between vehicle region between domain is come to first area Feature carries out Fusion Features, obtains first area characteristic value.Wherein, first area characteristic value may include at least one type The characteristic value of (such as can be position, color etc.).Similar, second area characteristic value can also be obtained.
4th step calculates Sample Similarity according to the difference between above-mentioned first area characteristic value and second area characteristic value Information.
Executing subject can compare first area characteristic value and second area characteristic value obtains difference.Then, executing subject Sample Similarity information can be calculated according to difference.For example, it is similar that sample can be set when difference is greater than setting first threshold It is low for spending information；When difference, which is greater than second threshold, is less than first threshold, can be set during Sample Similarity information is；Work as difference When less than second threshold, Sample Similarity information can be set as height.Wherein, first threshold can be greater than second threshold.
Step 206, according to the first vehicle similarity information and the second vehicle similarity information, described the is calculated Similarity between the corresponding vehicle of one vehicle image and the corresponding vehicle of the second vehicle image.
After obtaining the first vehicle similarity information and the second vehicle similarity information, executing subject can be according to the first vehicle Similarity information and the second vehicle similarity information are corresponding to calculate the corresponding vehicle of the first vehicle image and the second vehicle image Vehicle between similarity.
It is described according to the first vehicle similarity information and described in some optional implementations of the present embodiment Second vehicle similarity information calculates between the corresponding vehicle of first vehicle image and the corresponding vehicle of the second vehicle image Similarity, may comprise steps of:
The first step, the first power of the respectively described first vehicle similarity information and the second vehicle similarity information setting Weight and the second weight.
The local feature and global characteristics of image in order to balance, executing subject can be respectively the first vehicle similarity The first weight and the second weight is arranged in information and the second vehicle similarity information.First weight and the specific of the second weight take Value can be depending on actual needs.
Second step, by the first product of first weight and the first vehicle similarity information, with the second weight and second Sum between second product of vehicle similarity information is as the similarity between vehicle.
Executing subject can calculate separately the first product of the first weight and the first vehicle similarity information, the second weight and Second product of the second vehicle similarity information.Then, executing subject can be using the sum of first the second product of sum of products as vehicle Similarity between.
With continued reference to Fig. 3 a, Fig. 3 a is the application scenarios according to the method for obtaining vehicle similarity of the present embodiment One schematic diagram.Fig. 3 a is the first image to be processed, and Fig. 3 b is the second image to be processed of corresponding diagram 3a.Executing subject (such as Image collecting device 101 shown in FIG. 1) first area and black of a white box can be chosen in the first image to be processed The first area of frame；The second area an of white box and the secondth area of black box can be chosen in the second image to be processed Domain.Wherein, the second area of the first area of white box and white box is by car roof area, rear window region and vehicle rear window region together Juncture area.The first area of black box and the second area of black box are all Rear lamp for vehicle region.Then, executing subject is distinguished It is obtained from the second area of the first area of white box and white box, the first area of black box and the second area of black box First vehicle characteristic information and the second vehicle characteristic information.Later, according to the first vehicle characteristic information of each first area With the similarity of the second vehicle characteristic information of each corresponding second area, the first vehicle similarity information is obtained；It executes Main body obtains the third vehicle characteristic information and the 4th vehicle characteristics letter of the first image to be processed and the second image to be processed respectively Breath；Third vehicle characteristic information and the 4th vehicle characteristic information are imported into vehicle identification model, obtain the second vehicle similarity letter Breath；Finally, calculating the corresponding vehicle of the first vehicle image according to the first vehicle similarity information and the second vehicle similarity information And the second similarity between the corresponding vehicle of vehicle image may is that 0.9.
The method provided by the above embodiment of the disclosure, firstly, respectively from the first image to be processed and the second figure to be processed As choosing multiple first areas and multiple second areas, obtained respectively from each of the multiple first area first area First vehicle characteristic information and from each of the multiple second area second area obtain the second vehicle characteristic information, root It is special according to the first vehicle characteristic information and the second vehicle of corresponding each second area of each first area The similarity of reference breath, obtains the first vehicle similarity information；Then, obtain respectively the described first image to be processed and second to The third vehicle characteristic information and the 4th vehicle characteristic information for handling image, by the third vehicle characteristic information and the 4th vehicle Characteristic information imports vehicle identification model trained in advance, obtains the second vehicle similarity information；Finally, according to first vehicle Similarity information and the second vehicle similarity information calculate the corresponding vehicle of first vehicle image and the second vehicle Similarity between the corresponding vehicle of image.The technical solution of the application improves the accuracy to vehicle identification.
With further reference to Fig. 4, it illustrates the processes 400 of another embodiment of vehicle identification model training method.It should The process 400 of vehicle identification model training method, comprising the following steps:
Step 401, each sample in multiple sample vehicle image groups and corresponding above-mentioned multiple sample vehicle image groups is obtained The corresponding first sample vehicle characteristic information of vehicle image group, the second sample vehicle characteristic information and Sample Similarity information.
In the present embodiment, executing subject (such as the image collecting device shown in FIG. 1 of vehicle identification model training method 101 and/or server 103) it is every in available multiple sample vehicle image groups and corresponding above-mentioned multiple sample vehicle image groups The corresponding first sample vehicle characteristic information of a sample vehicle image group, the second sample vehicle characteristic information and Sample Similarity letter Breath.
Step 402, by the first sample vehicle of each sample vehicle image group in above-mentioned multiple sample vehicle image groups Characteristic information and the second sample vehicle characteristic information are sequentially input to initial vehicle identification model, obtain above-mentioned multiple sample vehicles Prediction similarity information corresponding to each sample vehicle image group in image group.
Executing subject can be by the first sample vehicle of each sample vehicle image group in multiple sample vehicle image groups Characteristic information and the second sample vehicle characteristic information are sequentially input to initial vehicle identification model, to obtain multiple sample vehicles Prediction similarity information corresponding to each sample vehicle image group in image group.Here, executing subject can be by each sample The first sample vehicle characteristic information of this vehicle image group and the second sample vehicle characteristic information are from initial vehicle identification model Input side input, successively by the processing of the parameter of each layer in initial vehicle identification model, and from initial vehicle identification model Outlet side output, outlet side output information be prediction similarity information corresponding to the sample vehicle image group.Wherein, The depth that initial vehicle identification model can be unbred deep learning model (convolutional neural networks etc.) or training is not completed Learning model is spent, each layer is provided with initiation parameter, and initiation parameter can be by the training process of deep learning model It continuously adjusts.
Step 403, by prediction phase corresponding to each sample vehicle image group in above-mentioned multiple sample vehicle image groups It is compared like degree information with Sample Similarity information corresponding to the sample vehicle image group, obtains above-mentioned initial vehicle identification The predictablity rate of model.
In the present embodiment, based on each sample vehicle figure in the obtained multiple sample vehicle image groups of step 402 As the corresponding prediction similarity information of group, executing subject can be by each sample vehicle figure in multiple sample vehicle image groups The prediction similarity information as corresponding to group is compared with Sample Similarity information corresponding to the sample vehicle image group, from And obtain the predictablity rate of initial vehicle identification model.
Step 404, determine whether above-mentioned predictablity rate is greater than default accuracy rate threshold value.
In the present embodiment, the predictablity rate based on the obtained initial vehicle identification model of step 403, executing subject The predictablity rate of initial vehicle identification model can be compared with default accuracy rate threshold value.If more than default accuracy rate threshold Value, thens follow the steps 405；If thening follow the steps 406 no more than default accuracy rate threshold value.
Step 405, the vehicle identification model above-mentioned initial vehicle identification model completed as training.
In the present embodiment, the case where the predictablity rate of initial vehicle identification model is greater than default accuracy rate threshold value Under, illustrate that the vehicle identification model training is completed, at this point, executing subject can be completed initial vehicle identification model as training Vehicle identification model.
Step 406, the parameter of above-mentioned initial vehicle identification model is adjusted.
In the present embodiment, the case where the predictablity rate of initial vehicle identification model is not more than default accuracy rate threshold value Under, the parameter of the adjustable initial vehicle identification model of executing subject, and return to step 402.
With further reference to Fig. 5, as the realization to method shown in above-mentioned each figure, present disclose provides one kind for obtaining vehicle One embodiment of the device of similarity, the Installation practice is corresponding with embodiment of the method shown in Fig. 2, and the device is specific It can be applied in various electronic equipments.
As shown in figure 5, the device 500 for obtaining vehicle similarity of the present embodiment may include: image-region selection Unit 501, fisrt feature information acquisition unit 502, the first vehicle similarity information acquiring unit 503, second feature information obtain Take unit 504, the second vehicle similarity information acquiring unit 505 and similarity calculated 506.Wherein, image-region selects Unit 501 is configured to choose multiple first areas and multiple the from the first image to be processed and the second image to be processed respectively Two regions, wherein first image to be processed includes the first vehicle image, and the second image to be processed includes the second vehicle figure Picture, each second area in the vehicle location and the multiple second area in the multiple first area in each first area Interior vehicle location is corresponding；Fisrt feature information acquisition unit 502 is configured to respectively from every in the multiple first area One first area obtains the first vehicle characteristic information and obtains the from each of the multiple second area second area Two vehicle characteristic informations；First vehicle similarity information acquiring unit 503 is configured to according to each first area The similarity of first vehicle characteristic information and the second vehicle characteristic information of corresponding each second area obtains first Vehicle similarity information；Second feature information acquisition unit 504 is configured to obtain the described first image to be processed and respectively The third vehicle characteristic information and the 4th vehicle characteristic information of two images to be processed；Second vehicle similarity information acquiring unit 505, it is configured to the third vehicle characteristic information and the 4th vehicle characteristic information importing vehicle identification mould trained in advance Type obtains the second vehicle similarity information；Similarity calculated 506 is configured to be believed according to the first vehicle similarity Breath and the second vehicle similarity information, calculate the corresponding vehicle of first vehicle image and the second vehicle image is corresponding Similarity between vehicle.
In some optional implementations of the present embodiment, the first vehicle similarity information acquiring unit 503 can To include: that sub- similarity information computation subunit (not shown) and the first vehicle similarity information obtain subelement (in figure It is not shown).Wherein, sub- similarity information computation subunit, it is special for multiple first vehicle characteristic informations and multiple second vehicles The first vehicle characteristic information and the second vehicle characteristic information in reference breath, are configured to calculate the first vehicle characteristic information and right It should sub- similarity information between the second vehicle characteristic information of the first vehicle characteristic information；First vehicle similarity information obtains Subelement is taken to be configured to using the sum of multiple sub- similarity informations as the first vehicle similarity information.
In some optional implementations of the present embodiment, the above-mentioned device 500 for obtaining vehicle similarity may be used also To include vehicle identification model training unit (not shown), it is configured to train vehicle identification model, above-mentioned vehicle identification Model training unit may include: that sample information obtains subelement (not shown) and vehicle identification model training subelement (not shown).Wherein, sample information acquisition subelement is configured to obtain multiple sample vehicle image groups and correspondence is above-mentioned The corresponding first sample vehicle characteristic information of each sample vehicle image group, the second sample vehicle in multiple sample vehicle image groups Characteristic information and Sample Similarity information, wherein sample vehicle image group includes first sample vehicle comprising vehicle image Image and the second sample vehicle subgraph comprising vehicle image, first sample vehicle subgraph and first sample vehicle characteristics are believed Breath corresponds to, and the second sample vehicle subgraph and the second sample vehicle characteristic information, Sample Similarity information is for characterizing the first sample Similarity between this vehicle subgraph and the second sample vehicle subgraph, sample vehicle characteristic information include following at least one : vehicle wheel profile, vehicle region line；Vehicle identification model training subelement is configured to above-mentioned multiple sample vehicle images The first sample vehicle characteristic information and the second sample vehicle characteristic information of each sample vehicle image group of group, will as input Above-mentioned Sample Similarity information corresponding to each sample vehicle image group in above-mentioned multiple sample vehicle image groups is as defeated Out, training obtains above-mentioned vehicle identification model.
In some optional implementations of the present embodiment, above-mentioned vehicle identification model training subelement may include: Vehicle identification model training module (not shown) is configured to each sample in above-mentioned multiple sample vehicle image groups The first sample vehicle characteristic information of vehicle image group and the second sample vehicle characteristic information sequentially input to initial vehicle and identify Model obtains prediction similarity information corresponding to each sample vehicle image group in above-mentioned multiple sample vehicle image groups, By prediction similarity information corresponding to each sample vehicle image group in above-mentioned multiple sample vehicle image groups and the sample Sample Similarity information corresponding to vehicle image group is compared, and the prediction for obtaining above-mentioned initial vehicle identification model is accurate Rate, determines whether above-mentioned predictablity rate is greater than default accuracy rate threshold value, then will be above-mentioned if more than above-mentioned default accuracy rate threshold value The vehicle identification model that initial vehicle identification model is completed as training.
In some optional implementations of the present embodiment, above-mentioned vehicle identification model training subelement may include: Parameter adjustment module (not shown) is configured to adjust above-mentioned initial in response to being not more than above-mentioned default accuracy rate threshold value The parameter of vehicle identification model, and return to vehicle identification model training module.
In some optional implementations of the present embodiment, the above-mentioned device 500 for obtaining vehicle similarity may be used also To include Sample Similarity information acquisition unit (not shown), it is configured to obtain Sample Similarity information, above-mentioned sample Similarity information acquiring unit may include: that sample vehicle region obtains subelement (not shown), provincial characteristics obtains son Unit (not shown), regional characteristic value obtain subelement (not shown) and Sample Similarity information computation subunit (not shown).Wherein, sample vehicle region obtains subelement and is configured to include according to sample vehicle image group first Sample vehicle subgraph and the corresponding first sample vehicle characteristic information of the second sample vehicle subgraph and the second sample vehicle Characteristic information determines the second vehicle region of the first vehicle region of sample and sample；Provincial characteristics obtains subelement and is configured ingredient The first area feature of the first vehicle region of sample of above-mentioned the first vehicle region of sample and the second vehicle region of sample is not obtained With second area feature, wherein provincial characteristics includes at least one of the following: lines assemblage characteristic, region assemblage characteristic；Region is special Value indicative obtains subelement and is configured to carry out Fusion Features to above-mentioned first area feature and second area feature respectively, obtains the One regional characteristic value and second area characteristic value；Sample Similarity information computation subunit is configured to according to above-mentioned first area Difference between characteristic value and second area characteristic value calculates Sample Similarity information.
In some optional implementations of the present embodiment, the similarity calculated 506 may include: that weight is set Set subelement (not shown) and similarity calculation subelement (not shown).Wherein, weight setting subelement is configured At the respectively described first vehicle similarity information and the second vehicle similarity information, the first weight and the second weight are set； Similarity calculation subelement is configured to the first product of first weight and the first vehicle similarity information, with the second power Sum between weight and the second product of the second vehicle similarity information is as the similarity between vehicle.
The present embodiment additionally provides a kind of electronic equipment, comprising: one or more processors；Memory is stored thereon with One or more programs, when said one or multiple programs are executed by said one or multiple processors, so that said one Or multiple processors execute the above-mentioned method for obtaining vehicle similarity.
The present embodiment additionally provides a kind of computer-readable medium, is stored thereon with computer program, and the program is processed Device realizes the above-mentioned method for obtaining vehicle similarity when executing.
Below with reference to Fig. 6, it illustrates the electronic equipments for being suitable for being used to realize embodiment of the disclosure (for example, in Fig. 1 Server 103) computer system 600 structural schematic diagram.Electronic equipment shown in Fig. 6 is only an example, should not be right The function and use scope of embodiment of the disclosure bring any restrictions.
As shown in fig. 6, electronic equipment 600 may include processing unit (such as central processing unit, graphics processor etc.) 601, random access can be loaded into according to the program being stored in read-only memory (ROM) 602 or from storage device 608 Program in memory (RAM) 603 and execute various movements appropriate and processing.In RAM 603, it is also stored with electronic equipment Various programs and data needed for 600 operations.Processing unit 601, ROM 602 and RAM 603 pass through the phase each other of bus 604 Even.Input/output (I/O) interface 605 is also connected to bus 604.
In general, following device can connect to I/O interface 605: including such as touch screen, touch tablet, keyboard, mouse, taking the photograph As the input unit 606 of head, microphone, accelerometer, gyroscope etc.；Including such as liquid crystal display (LCD), loudspeaker, vibration The output device 607 of dynamic device etc.；Storage device 608 including such as tape, hard disk etc.；And communication device 609.Communication device 609, which can permit electronic equipment 600, is wirelessly or non-wirelessly communicated with other equipment to exchange data.Although Fig. 6 shows tool There is the electronic equipment 600 of various devices, it should be understood that being not required for implementing or having all devices shown.It can be with Alternatively implement or have more or fewer devices.Each box shown in Fig. 6 can represent a device, can also root According to needing to represent multiple devices.
Particularly, in accordance with an embodiment of the present disclosure, it may be implemented as computer above with reference to the process of flow chart description Software program.For example, embodiment of the disclosure includes a kind of computer program product comprising be carried on computer-readable medium On computer program, which includes the program code for method shown in execution flow chart.In such reality It applies in example, which can be downloaded and installed from network by communication device 609, or from storage device 608 It is mounted, or is mounted from ROM 602.When the computer program is executed by processing unit 601, the implementation of the disclosure is executed The above-mentioned function of being limited in the method for example.
It is situated between it should be noted that the above-mentioned computer-readable medium of embodiment of the disclosure can be computer-readable signal Matter or computer readable storage medium either the two any combination.Computer readable storage medium for example can be with System, device or the device of --- but being not limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, or it is any more than Combination.The more specific example of computer readable storage medium can include but is not limited to: have one or more conducting wires Electrical connection, portable computer diskette, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type are programmable Read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic are deposited Memory device or above-mentioned any appropriate combination.In embodiment of the disclosure, computer readable storage medium, which can be, appoints What include or the tangible medium of storage program that the program can be commanded execution system, device or device use or and its It is used in combination.And in embodiment of the disclosure, computer-readable signal media may include in a base band or as carrier wave The data-signal that a part is propagated, wherein carrying computer-readable program code.The data-signal of this propagation can be adopted With diversified forms, including but not limited to electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal is situated between Matter can also be any computer-readable medium other than computer readable storage medium, which can be with It sends, propagate or transmits for by the use of instruction execution system, device or device or program in connection.Meter The program code for including on calculation machine readable medium can transmit with any suitable medium, including but not limited to: electric wire, optical cable, RF (radio frequency) etc. or above-mentioned any appropriate combination.
Above-mentioned computer-readable medium can be included in above-mentioned electronic equipment；It is also possible to individualism, and not It is fitted into the electronic equipment.Above-mentioned computer-readable medium carries one or more program, when said one or more When a program is executed by the electronic equipment, so that the electronic equipment: respectively from the first image to be processed and the second image to be processed Choose multiple first areas and multiple second areas, wherein first image to be processed include the first vehicle image, second to Handling image includes the second vehicle image, vehicle location in the multiple first area in each first area and the multiple Vehicle location in second area in each second area is corresponding；Respectively from the firstth area of each of the multiple first area Domain obtains the first vehicle characteristic information and obtains the second vehicle characteristics from each of the multiple second area second area Information；According to the first vehicle characteristic information of each first area and the second of corresponding each second area The similarity of vehicle characteristic information obtains the first vehicle similarity information；The described first image to be processed and second are obtained respectively The third vehicle characteristic information and the 4th vehicle characteristic information of image to be processed；By the third vehicle characteristic information and the 4th vehicle Characteristic information imports vehicle identification model trained in advance, obtains the second vehicle similarity information；According to first vehicle Similarity information and the second vehicle similarity information calculate the corresponding vehicle of first vehicle image and the second vehicle figure As the similarity between corresponding vehicle.
The behaviour for executing embodiment of the disclosure can be write with one or more programming languages or combinations thereof The computer program code of work, above procedure design language include object oriented program language-such as Java, Smalltalk, C++ further include conventional procedural programming language-such as " C " language or similar program design language Speech.Program code can be executed fully on the user computer, partly be executed on the user computer, as an independence Software package execute, part on the user computer part execute on the remote computer or completely in remote computer or It is executed on server.In situations involving remote computers, remote computer can pass through the network of any kind --- packet It includes local area network (LAN) or wide area network (WAN)-is connected to subscriber computer, or, it may be connected to outer computer (such as benefit It is connected with ISP by internet).
Flow chart and block diagram in attached drawing illustrate system, method and the computer of the various embodiments according to the disclosure The architecture, function and operation in the cards of program product.In this regard, each box in flowchart or block diagram can be with A part of a module, program segment or code is represented, a part of the module, program segment or code includes one or more Executable instruction for implementing the specified logical function.It should also be noted that in some implementations as replacements, institute in box The function of mark can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are practical On can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it wants It is noted that the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart, Ke Yiyong The dedicated hardware based system of defined functions or operations is executed to realize, or can be referred to specialized hardware and computer The combination of order is realized.
Being described in unit involved in embodiment of the disclosure can be realized by way of software, can also be passed through The mode of hardware is realized.Described unit also can be set in the processor, for example, can be described as: a kind of processor Including image-region selecting unit, fisrt feature information acquisition unit, the first vehicle similarity information acquiring unit, second feature Information acquisition unit, the second vehicle similarity information acquiring unit and similarity calculated.Wherein, the title of these units exists The restriction to the unit itself is not constituted in the case of certain, such as similarity calculated is also described as " for leading to It crosses the first vehicle similarity information and the second vehicle similarity information calculates the unit of similarity between two vehicle images ".
Above description is only the preferred embodiment of the disclosure and the explanation to institute's application technology principle.Those skilled in the art Member is it should be appreciated that invention scope involved in the disclosure, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic Scheme, while should also cover in the case where not departing from foregoing invention design, it is carried out by above-mentioned technical characteristic or its equivalent feature Any combination and the other technical solutions formed.Such as features described above has similar function with (but being not limited to) disclosed in the disclosure Can technical characteristic replaced mutually and the technical solution that is formed.

Claims (16)

1. a kind of method for obtaining vehicle similarity, comprising:
Multiple first areas and multiple second areas are chosen from the first image to be processed and the second image to be processed respectively, wherein First image to be processed includes the first vehicle image, and the second image to be processed includes the second vehicle image, and the multiple the Vehicle location in vehicle location and the multiple second area in one region in each first area in each second area It is corresponding；
From each of the multiple first area first area obtain the first vehicle characteristic information respectively and from the multiple Each of second area second area obtains the second vehicle characteristic information；
According to the first vehicle characteristic information of each first area and the second of corresponding each second area The similarity of vehicle characteristic information obtains the first vehicle similarity information；
Third vehicle characteristic information and the 4th vehicle for obtaining the described first image to be processed and the second image to be processed respectively are special Reference breath；
The third vehicle characteristic information and the 4th vehicle characteristic information are imported to vehicle identification model trained in advance, obtain the Two vehicle similarity informations；
According to the first vehicle similarity information and the second vehicle similarity information, first vehicle image pair is calculated Similarity between the vehicle answered and the corresponding vehicle of the second vehicle image.
2. according to the method described in claim 1, wherein, the first vehicle characteristics of each first area according to are believed Breath is believed with the similarity of the second vehicle characteristic information of corresponding each second area, the first vehicle similarity of acquisition Breath, comprising:
For the first vehicle characteristic information and second in multiple first vehicle characteristic informations and multiple second vehicle characteristic informations Vehicle characteristic information, calculate the first vehicle characteristic information and corresponding first vehicle characteristic information the second vehicle characteristic information it Between sub- similarity information；
Using the sum of multiple sub- similarity informations as the first vehicle similarity information.
3. according to the method described in claim 1, wherein, the vehicle identification model is obtained by following steps training:
Obtain each sample vehicle image group pair in multiple sample vehicle image groups and corresponding the multiple sample vehicle image group First sample vehicle characteristic information, the second sample vehicle characteristic information and the Sample Similarity information answered, wherein sample vehicle figure As group includes the first sample vehicle subgraph comprising vehicle image and the second sample vehicle subgraph comprising vehicle image, the One sample vehicle subgraph is corresponding with first sample vehicle characteristic information, and the second sample vehicle subgraph and the second sample vehicle are special Reference breath, Sample Similarity information are similar between first sample vehicle subgraph and the second sample vehicle subgraph for characterizing Degree, sample vehicle characteristic information include at least one of the following: vehicle wheel profile, vehicle region line；
By the first sample vehicle characteristic information and second of each sample vehicle image group of the multiple sample vehicle image group Sample vehicle characteristic information, will be corresponding to each sample vehicle image group in the multiple sample vehicle image group as input The Sample Similarity information as output, training obtain the vehicle identification model.
4. according to the method described in claim 3, wherein, each sample vehicle by the multiple sample vehicle image group The first sample vehicle characteristic information of image group and the second sample vehicle characteristic information are as input, by the multiple sample vehicle The Sample Similarity information corresponding to each sample vehicle image group in image group obtains the vehicle as output, training Identification model, comprising:
Execute following training step: by the first sample of each sample vehicle image group in the multiple sample vehicle image group Vehicle characteristic information and the second sample vehicle characteristic information are sequentially input to initial vehicle identification model, obtain the multiple sample Prediction similarity information corresponding to each sample vehicle image group in vehicle image group, by the multiple sample vehicle image Sample corresponding to prediction similarity information and the sample vehicle image group corresponding to each sample vehicle image group in group Similarity information is compared, and obtains the predictablity rate of the initial vehicle identification model, determines that the predictablity rate is It is no to be greater than default accuracy rate threshold value, if more than the default accuracy rate threshold value, then using the initial vehicle identification model as instruction Practice the vehicle identification model completed.
5. according to the method described in claim 4, wherein, each sample vehicle by the multiple sample vehicle image group The first sample vehicle characteristic information of image group and the second sample vehicle characteristic information are as input, by the multiple sample vehicle The Sample Similarity information corresponding to each sample vehicle image group in image group obtains the vehicle as output, training Identification model, comprising:
In response to being not more than the default accuracy rate threshold value, the parameter of the initial vehicle identification model is adjusted, and continue to execute The training step.
6. according to the method described in claim 3, wherein, the Sample Similarity information is obtained by following steps:
The first sample vehicle subgraph and the second sample vehicle subgraph for including according to sample vehicle image group are corresponding First sample vehicle characteristic information and the second sample vehicle characteristic information determine the second vehicle of the first vehicle region of sample and sample Region；
The firstth area of the first vehicle region of sample of first vehicle region of sample and the second vehicle region of sample is obtained respectively Characteristic of field and second area feature, wherein provincial characteristics includes at least one of the following: lines assemblage characteristic, region assemblage characteristic；
Fusion Features are carried out to the first area feature and second area feature respectively, obtain first area characteristic value and second Regional characteristic value；
Sample Similarity information is calculated according to the difference between the first area characteristic value and second area characteristic value.
7. according to claim 1 to method described in 6 any one, wherein described according to the first vehicle similarity information With the second vehicle similarity information, the corresponding vehicle of first vehicle image and the corresponding vehicle of the second vehicle image are calculated Similarity between, comprising:
The first weight and the second power is arranged in the respectively described first vehicle similarity information and the second vehicle similarity information Weight；
By the first product of first weight and the first vehicle similarity information, believe with the second weight and the second vehicle similarity Sum between second product of breath is as the similarity between vehicle.
8. a kind of for obtaining the device of vehicle similarity, comprising:
Image-region selecting unit is configured to choose multiple first from the first image to be processed and the second image to be processed respectively Region and multiple second areas, wherein first image to be processed includes the first vehicle image, and the second image to be processed includes Second vehicle image, it is every in the vehicle location and the multiple second area in the multiple first area in each first area Vehicle location in a second area is corresponding；
Fisrt feature information acquisition unit is configured to obtain from each of the multiple first area first area respectively First vehicle characteristic information and from each of the multiple second area second area obtain the second vehicle characteristic information；
First vehicle similarity information acquiring unit is configured to the first vehicle characteristics letter according to each first area Breath is believed with the similarity of the second vehicle characteristic information of corresponding each second area, the first vehicle similarity of acquisition Breath；
Second feature information acquisition unit is configured to obtain the described first image to be processed and the second image to be processed respectively Third vehicle characteristic information and the 4th vehicle characteristic information；
Second vehicle similarity information acquiring unit is configured to believe the third vehicle characteristic information and the 4th vehicle characteristics Breath imports vehicle identification model trained in advance, obtains the second vehicle similarity information；
Similarity calculated is configured to be believed according to the first vehicle similarity information and the second vehicle similarity Breath calculates the similarity between the corresponding vehicle of first vehicle image and the corresponding vehicle of the second vehicle image.
9. device according to claim 8, wherein the first vehicle similarity information acquiring unit includes:
Sub- similarity information computation subunit, in multiple first vehicle characteristic informations and multiple second vehicle characteristic informations First vehicle characteristic information and the second vehicle characteristic information are configured to calculate the first vehicle characteristic information and corresponding first vehicle Sub- similarity information between second vehicle characteristic information of characteristic information；
First vehicle similarity information obtains subelement, is configured to using the sum of multiple sub- similarity informations as the first vehicle phase Like degree information.
10. device according to claim 8, wherein described device further includes vehicle identification model training unit, is configured At training vehicle identification model, the vehicle identification model training unit includes:
Sample information obtains subelement, is configured to obtain multiple sample vehicle image groups and corresponding the multiple sample vehicle figure As the corresponding first sample vehicle characteristic information of sample vehicle image group each in group, the second sample vehicle characteristic information and sample Similarity information, wherein sample vehicle image group include comprising vehicle image first sample vehicle subgraph and comprising vehicle Second sample vehicle subgraph of image, first sample vehicle subgraph is corresponding with first sample vehicle characteristic information, the second sample This vehicle subgraph and the second sample vehicle characteristic information, Sample Similarity information for characterize first sample vehicle subgraph and Similarity between second sample vehicle subgraph, sample vehicle characteristic information include at least one of the following: vehicle wheel profile, vehicle Region line；
Vehicle identification model training subelement is configured to each sample vehicle image of the multiple sample vehicle image group The first sample vehicle characteristic information and the second sample vehicle characteristic information of group are as input, by the multiple sample vehicle image As output, training obtains the vehicle and knows the Sample Similarity information corresponding to each sample vehicle image group in group Other model.
11. device according to claim 10, wherein the vehicle identification model training subelement includes:
Vehicle identification model training module is configured to each sample vehicle image in the multiple sample vehicle image group The first sample vehicle characteristic information and the second sample vehicle characteristic information of group are sequentially input to initial vehicle identification model, are obtained Prediction similarity information corresponding to each sample vehicle image group in the multiple sample vehicle image group, will be the multiple Prediction similarity information corresponding to each sample vehicle image group in sample vehicle image group and the sample vehicle image group Corresponding Sample Similarity information is compared, and obtains the predictablity rate of the initial vehicle identification model, determine described in Whether predictablity rate is greater than default accuracy rate threshold value, if more than the default accuracy rate threshold value, then knows the initial vehicle The vehicle identification model that other model is completed as training.
12. device according to claim 11, wherein the vehicle identification model training subelement includes:
Parameter adjustment module is configured to adjust the initial vehicle identification in response to being not more than the default accuracy rate threshold value The parameter of model, and return to vehicle identification model training module.
13. device according to claim 10, wherein described device further includes Sample Similarity information acquisition unit, quilt It is configured to obtain Sample Similarity information, the Sample Similarity information acquisition unit includes:
Sample vehicle region obtains subelement, is configured to the first sample vehicle subgraph for including according to sample vehicle image group First sample vehicle characteristic information corresponding with the second sample vehicle subgraph and the second sample vehicle characteristic information determine The second vehicle region of the first vehicle region of sample and sample；
Provincial characteristics obtains subelement, is configured to obtain first vehicle region of sample and the second vehicle region of sample respectively The first vehicle region of sample first area feature and second area feature, wherein provincial characteristics includes at least one of the following: Lines assemblage characteristic, region assemblage characteristic；
Regional characteristic value obtains subelement, is configured to carry out feature to the first area feature and second area feature respectively Fusion, obtains first area characteristic value and second area characteristic value；
Sample Similarity information computation subunit, be configured to according to the first area characteristic value and second area characteristic value it Between difference calculate Sample Similarity information.
14. according to device described in claim 8 to 13 any one, wherein the similarity calculated includes:
Subelement is arranged in weight, is configured to the respectively described first vehicle similarity information and the second vehicle similarity letter Breath the first weight of setting and the second weight；
Similarity calculation subelement, is configured to the first product of first weight and the first vehicle similarity information, with Sum between second weight and the second product of the second vehicle similarity information is as the similarity between vehicle.
15. a kind of electronic equipment, comprising:
One or more processors；
Memory is stored thereon with one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors Perform claim requires any method in 1 to 7.
16. a kind of computer-readable medium, is stored thereon with computer program, which is characterized in that the program is executed by processor Method of the Shi Shixian as described in any in claim 1 to 7.
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