CN109583505A - A kind of object correlating method, device, equipment and the medium of multisensor - Google Patents
A kind of object correlating method, device, equipment and the medium of multisensor Download PDFInfo
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Abstract
The invention discloses object correlating method, device, equipment and the media of a kind of multisensor, which comprises at least one the fusion track data collection for obtaining at least one being currently generated sensor track data collection to be matched and having saved;Wherein, respectively fusion track data collection includes at least one existing sensor track data collection;Determine sensor track data collection to be matched with merge the similarity between track data collection;According to similarity definitive result, the incidence relation between the corresponding object of sensor track data collection to be matched object corresponding with fusion track data collection is determined;Through the above technical solutions, improving the association accuracy and association efficiency between the same object from different sensors.
Description
Technical field
The present embodiments relate to Data Fusion of Sensor technology more particularly to a kind of object affiliated parties of multisensor
Method, device, equipment and medium.
Background technique
Data Fusion of Sensor is a core topic of current robot and its relevant technologies.In actual environment only
The whole metrical informations for wanting the object of measurement can not be accurately obtained by single-sensor.Different sensors are in difference
The short length of aspect mutual.Some sensors such as video camera can accurately identify the type of object, while the visual field is remote enough, but
It is the position for object, the measurement effect of the information such as speed is then poor.Laser radar sensor then has pole to object space
High measurement accuracy, but due to the characteristic of sensor itself, can only at most accomplish that 128 lines, a line can only measure at present
A plane in 3d space, causes a cloud sparse, it is contemplated that can identify the accuracy rate of object, therefore effectively measure distance
It is significantly less than sensor itself and measures distance.And millimetre-wave radar then aligns the tachometric survey of object according to Doppler effect
Really, but it can only obtain the 2D location information of object.So by the obtained data knot for the same object of different sensors
Obtain altogether each attribute an of object precise information just become extremely it is necessary to.Sensor fusion algorithm is exactly purport
Solving the problems, such as this kind.The primary data information (pdi) that then fusion exactly obtains each sensor is by after preliminary treatment
The object of abstract chemical conversion one by one, each sensor obtains each object information in the same space, for example, in current nothing
The vehicle and pedestrian information in road ahead can be then obtained in the perception of people's vehicle.The association algorithm merged afterwards is that the same object exists
Object information obtained in different sensors sets up the algorithm of one-to-one relationship.Fusion association algorithm is melted after being based on afterwards
The first step of the sensory perceptual system of conjunction, and the entire rear foundation stone merged.If mistake occurs in association, subsequent speed is melted
It closes, mistake can all occur in Co-factor propagation etc..Therefore improve after merge association algorithm accuracy become pole it is necessary to.
The rear fusion association algorithm of current mainstream is then to provide not simultaneous interpretation according to this feature by constructing certain feature
Similarity between the object that sensor obtains under certain condition metric.Later according to global similarity maximum principle to not simultaneous interpretation
The similarity of the different objects of sensor is cumulative, and the maximum result of overall similarity is exactly to merge associated result.
The feature of fusion association algorithm building is generally following several afterwards:
The position of testee.The position for the same object that different sensors obtain theoretically should be identical.
The distribution of the standard deviation of measurement according to measurement sensor is available under current measuring condition, and two objects are the same objects
The probability of body.
The speed of testee.The speed of testee include speed direction and size, according to the similarity of vector come
Calculate the likelihood probability of two objects.
Projection of the point cloud of the laser radar of testee on camera.It calculates point cloud and is projected in the point in some detection block
The percentage of Yun Zhandian cloud sum determines similarity.
Projection of the position that the millimetre-wave radar of testee obtains on camera.Calculate the position the 3D frame that millimeter wave obtains
In the projection of camera plane and the ROI (hand over and compare) of image detection frame.
In the prior art, sensor-based frame data calculates object similarity, according to object similarity to not simultaneous interpretation
The object of sensor perception is matched, and merges association results after obtaining.The information obtained due to frame data is largely all
The positional relationship that can exist between more or less noise and sensor is changed as the variation of time has uncertainty,
System deviation or random noise are thus introduced, and existing rear fusion association algorithm can all design a threshold value, once it is similar
Degree just will not add it among matching algorithm lower than this threshold value, but since the presence of noise will lead to similarity on side
The matching of edge floats up and down with the variation of noise in threshold value, and then the appearance that the matching will not be stable, after eventually leading to entirety
The reduction of blending algorithm performance.And when using different features, between feature not in the same metric space, therefore not
Weight with feature is very difficult to determine.
As it can be seen that the side that the object that fusion association algorithm perceives different sensors after using in the prior art is associated
Case, accuracy are lower.
Summary of the invention
The present invention provides object correlating method, device, equipment and the medium of a kind of multisensor, comes from not simultaneous interpretation to improve
Association accuracy and association efficiency between the same object of sensor.
In a first aspect, the embodiment of the invention provides a kind of object correlating methods of multisensor, which comprises
At least one for obtaining at least one the sensor track data collection to be matched being currently generated and having saved is melted
Close track data collection;Wherein, respectively fusion track data collection includes at least one existing sensor track data collection;
Determine sensor track data collection to be matched with merge the similarity between track data collection;
According to similarity definitive result, determine the corresponding object of sensor track data collection to be matched with merge track number
According to the incidence relation collected between corresponding object;
Wherein, each sensor track data collection includes the sensing data of the setting number continuously exported according to same sensor
The characteristic for the same object that frame determines respectively, object corresponding to different sensors track data collection is different, same fusion
The identical and corresponding sensor of object corresponding to each sensor track data collection that track data collection includes is different, and
Object corresponding to difference fusion track data collection is different.
Second aspect, the embodiment of the invention also provides a kind of object associated apparatus of multisensor, described device includes:
Module is obtained, for obtaining at least one the sensor track data collection to be matched being currently generated and having saved
At least one fusion track data collection;Wherein, respectively fusion track data collection includes at least one existing sensor track number
According to collection;
Similarity determining module, for determine sensor track data collection to be matched with merge between track data collection
Similarity;
Incidence relation determining module, for determining sensor track data collection to be matched according to similarity definitive result
Incidence relation between corresponding object object corresponding with fusion track data collection;
Wherein, each sensor track data collection includes the sensing data of the setting number continuously exported according to same sensor
The characteristic for the same object that frame determines respectively, object corresponding to different sensors track data collection is different, same fusion
The identical and corresponding sensor of object corresponding to each sensor track data collection that track data collection includes is different, and
Object corresponding to difference fusion track data collection is different.
The third aspect, the embodiment of the invention also provides a kind of electronic equipment, the electronic equipment includes:
One or more processors;
Storage device, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processing
Device realizes the object correlating method of the multisensor as described in any in claim 1-11.
Fourth aspect, the embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer
Program, which is characterized in that realize when the program is executed by processor such as the multisensor as described in any in claim 1-11
Object correlating method.
The present invention is by obtaining at least one sensor track data collection to be matched for being currently generated and having saved
At least one fusion track data collection;Wherein, respectively fusion track data collection includes at least one existing sensor track data
Collection;Determine sensor track data collection to be matched with merge the similarity between track data collection;It is determined and is tied according to similarity
Fruit determines the association between the corresponding object of sensor track data collection to be matched object corresponding with fusion track data collection
Relationship, wherein each sensor track data collection includes the sensing data frame of the setting number continuously exported according to same sensor
The characteristic of determining same object respectively, object corresponding to different sensors track data collection is different, same fusion rail
The identical and corresponding sensor of object corresponding to each sensor track data collection that mark data set includes is different, and not
With object corresponding to fusion track data collection, different technological means, is improved between the same object from different sensors
Association accuracy and association efficiency.
Detailed description of the invention
Fig. 1 is the flow diagram of the object correlating method of one of the embodiment of the present invention one multisensor;
Fig. 2 is the flow diagram of the object correlating method of one of the embodiment of the present invention two multisensor;
Fig. 3 is the flow diagram of the object correlating method of one of the embodiment of the present invention three multisensor;
Fig. 4 is the structural schematic diagram of the object associated apparatus of one of the embodiment of the present invention four multisensor;
Fig. 5 is the structural schematic diagram of one of the embodiment of the present invention five electronic equipment.
Specific embodiment
The present invention 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 the present invention rather than limiting the invention.It also should be noted that in order to just
Only the parts related to the present invention are shown in description, attached drawing rather than entire infrastructure.
Embodiment one
Fig. 1 is a kind of flow diagram of the object correlating method for multisensor that the embodiment of the present invention one provides, this reality
It applies example to be applicable to merge based on the data that multiple sensors simultaneously detect multiple objects, to realize in the future
The case where being associated from the data for same object of multiple sensors, this method can be associated with by the object of multisensor
Device executes, which can realize that described device is generally integrated in terminal by the mode of software and/or hardware, the end
End for example specifically can be server.A kind of flow diagram of the object correlating method of multisensor shown in Figure 1, institute
Object correlating method is stated to specifically comprise the following steps:
Step 110, at least one sensor track data collection to be matched for being currently generated of acquisition and saved to
A few fusion track data collection;Wherein, respectively fusion track data collection includes at least one existing sensor track data collection.
Wherein, each sensor track data collection includes the sensing data of the setting number continuously exported according to same sensor
The characteristic for the same object that frame determines respectively, object corresponding to different sensors track data collection is different, same fusion
The identical and corresponding sensor of object corresponding to each sensor track data collection that track data collection includes is different, and
Object corresponding to difference fusion track data collection is different.The characteristic of the object for example can be speed data and/or
Position data.
It illustrates the sensor track data collection to be matched being related in above-mentioned steps 110 and has saved at least
The meaning of one fusion track data collection:
Assuming that configuration (is commonly configured on unmanned vehicle in practical application and is more than there are two different sensors on unmanned vehicle
Two different sensors, the present embodiment is to configure there are two being illustrated for different sensors, when configured with more than two
When a different sensors, process that the data from different sensors are merged with will illustrate to from two not
The process that same sensing data is merged is consistent), described two different sensors are respectively camera and radar, at nobody
Vehicle detects the driving scene of unmanned vehicle during travelling.Radar and camera are one frame sensing data of output per second
Frame, when same sensor (radar or camera) continuously exports 5 frame sensing data frame, carry out an object features data determines behaviour
Make, specifically the 5 frame sensing data frames that algorithm is continuously exported based on the same sensor can be determined using existing characteristic
Determine the characteristic of same object.It starts to work assuming that radar shifts to an earlier date 1s than camera, the time started to work by radar is in terms of
Shi Qidian, then camera is started to work from 2s, and in 5s, the sensing data frame that radar continuously exports reaches 5 frames, at this point, then
Object features identification is carried out based on the 5 frame sensing data frames that radar continuously exports, it is assumed that is wrapped in the sensing data frame of radar output
The detection data of object A and object B are included, then carries out object features identification by the 5 frame sensing data frames continuously exported to radar,
The sensor track data collection of object A is obtained respectively, is labeled as STL-AAnd the sensor track data collection of object B, it is labeled as
STL-B;However in 5s, the sensing data frame that camera continuously exports only has 4 frames, does not meet preset condition (5 frames of continuous output
Sensing data frame) therefore, object features identification is not carried out to the sensing data frame of camera output.As 6s, camera is continuously defeated
Sensing data frame out reaches 5 frames, at this point, carrying out feature identification to the 5 frame sensing data frames that camera continuously exports, it is assumed that camera
Include the detection data of object 1 and object 2 in the sensing data frame of output, then senses number by 5 frames continuously exported to camera
Object features identification is carried out according to frame, obtains the sensor track data collection of object 1 respectively, is labeled as STC-1And the biography of object 2
Sensor track data collection is labeled as STC-2.The object correlating method of multisensor provided in this embodiment is used for phase machine testing
To object 1 respectively with detections of radar to object A and object B be associated, to determine in object 1 and object A and object B
Which object belongs to the same object, and determines object 2 belongs to the same object with which object in object A and object B.
Therefore the sensor track data collection ST of object A that above-mentioned radar obtainsL-AWith the sensor track data collection ST of object BL-BRespectively
" the two fusion track data collection saved " in step 110 are formed, are denoted as fusion track data collection FT respectivelyAAnd FTB,
In, merge track data collection FTAInclude the sensor track data collection ST about object A from radarL-A, merge track number
According to collection FTBInclude the sensor track data collection ST about object B from radarL-B.Above-mentioned camera obtain about object 1
Sensor track data collection STC-1, and about the sensor track data collection ST of object 2C-2It is as current (6s when
Carve) generate two sensor track data collection to be matched.
Step 120, determine sensor track data collection to be matched with merge the similarity between track data collection.
The example above is continued to use, that is, determines sensor track data collection ST to be matchedC-1And STC-2Respectively with merge rail
Mark data set FTAAnd FTBBetween similarity.It specifically can be by calculating sensor track data collection STC-1With merge track data
Collect FTAIn similarity between each sensor track data collection determine sensor track data collection ST to be matchedC-1With merge
Track data collection FTABetween similarity, such as can be by sensor track data collection ST to be matchedC-1With merge track data
Collect FTAIn between each sensor track data collection similarity average value as sensor track data collection ST to be matchedC-1With
Merge track data collection FTABetween similarity.Sensor track data collection ST to be matchedC-1With merge track data collection FTB
Between the calculation of similarity, sensor track data collection ST to be matchedC-2With merge track data collection FTABetween it is similar
The calculation of degree and sensor track data collection ST to be matchedC-2With merge track data collection FTBBetween similarity meter
Calculation mode and above-mentioned sensor track data collection ST to be matchedC-1With merge track data collection FTABetween similarity calculating side
Formula is identical.
Step 130, according to similarity definitive result, determine the corresponding object of sensor track data collection to be matched and melt
Close the incidence relation between the corresponding object of track data collection.
Specifically, by the maximum value in the corresponding object of sensor track data collection and similarity definitive result to be matched
Incidence relation is established between the corresponding object of corresponding fusion track data collection.For example, it is assumed that above-mentioned sensor rail to be matched
Mark data set STC-1With merge track data collection FTABetween similarity be 0.6, sensor track data collection ST to be matchedC-2
With merge track data collection FTBBetween similarity be 0.2, then similarity definitive result be 0.6+0.2=0.8;Meanwhile to
The sensor track data collection ST matchedC-1With merge track data collection FTBBetween similarity be 0.3, sensor rail to be matched
Mark data set STC-2With merge track data collection FTABetween similarity be 0.2, then similarity definitive result be 0.3+0.2=
0.5;Since 0.8 greater than 0.5, by sensor track data collection ST to be matchedC-1Corresponding object 1 with merge track number
According to collection FTAIncidence relation is established between corresponding object A, by sensor track data collection ST to be matchedC-2Corresponding object 2
With merge track data collection FTBIncidence relation is established between corresponding object B.
The technical solution of the present embodiment, by obtaining at least one the sensor track data collection to be matched being currently generated
And at least one fusion track data collection saved;Wherein, respectively fusion track data collection includes at least one existing biography
Sensor track data collection;Determine sensor track data collection to be matched with merge the similarity between track data collection;According to
Similarity definitive result determines the corresponding object of sensor track data collection to be matched object corresponding with fusion track data collection
Incidence relation between body, wherein each sensor track data collection includes the setting number continuously exported according to same sensor
The characteristic of same object that determines respectively of sensing data frame, object corresponding to different sensors track data collection is not
Together, the identical and corresponding sensing of object corresponding to each sensor track data collection that same fusion track data collection includes
Device is different, and the technological means that object corresponding to different fusion track data collection is different, improves from different sensors
Same object between association accuracy and association efficiency.
Embodiment two
Fig. 2 is the flow diagram of the object correlating method of one of the embodiment of the present invention two multisensor, above-mentioned
On the basis of embodiment technical solution, " step 120 determines sensor track data to be matched this gives above-mentioned
Collection and merge the similarity between track data collection " a kind of specific embodiment, shown in Figure 2, the object affiliated party
Method includes the following steps:
Step 210, at least one sensor track data collection to be matched for being currently generated of acquisition and saved to
A few fusion track data collection;Wherein, respectively fusion track data collection includes at least one existing sensor track data collection.
Step 220, for each sensor track data collection to be matched, determine current sensor rail to be matched respectively
Each similarity merged between track data collection of mark data set and the condition that meets.
Wherein, sensing corresponding to each sensor track data collection that the fusion track data collection for meeting condition includes
Device is different from sensor corresponding to current sensor track data collection to be matched.For example, it is assumed that current sensing to be matched
Device track data collection is the sensor track data collection ST for object 1 that above-mentioned camera detectsC-1, due to above-mentioned fusion rail
Mark data set FTAThe sensor track data collection ST for includingL-ACorresponding sensor is radar, with current sensing to be matched
Device track data collection STC-1Corresponding camera sensor is different, therefore above-mentioned fusion track data collection FTAFor current biography to be matched
Sensor track data collection STC-1The fusion track data collection for meeting condition.Similarly, due to above-mentioned fusion track data collection FTBPacket
The sensor track data collection ST containedL-BCorresponding sensor is radar, with current sensor track data collection to be matched
STC-1Corresponding camera sensor is different, therefore above-mentioned fusion track data collection FTBIt also is current sensor track number to be matched
According to collection STC-1The fusion track data collection for meeting condition.
Illustratively, the current sensor track data collection to be matched of the determination merges track number with each of the condition that meets
According to the similarity between collection, comprising:
It calculates separately current sensor track data collection to be matched and merges track data Ji Bao with any of the condition that meets
The similarity between each sensor track data collection contained determines current sensor track to be matched according to the similarity of calculating
Any similarity merged between track data collection of data set and the condition that meets, for example, it is current to be matched by what is be calculated
Sensor track data collection merges between each sensor track data collection that track data collection includes with any of the condition that meets
Similarity is averaged, and is merged the average value as current sensor track data collection to be matched with any of the condition that meets
Similarity between track data collection.
Assuming that current sensor track data collection to be matched is the sensor rail for object 1 that above-mentioned camera detects
Mark data set STC-1, there are two the fusion track data collection that meet condition, respectively fusion track data collection FTAWith fusion track
Data set FTB, wherein fusion track data collection FTAIn include two sensor track data collection, be respectively as follows: the first detections of radar
The sensor track data collection ST for object A arrivedL1-A, and the sensor track for object A that the second detections of radar arrives
Data set STL2-A;Merge track data collection FTBIn include a sensor track data collection, for third detections of radar to be directed to
The sensor track data collection ST of object BL3-B;Then determine current sensor track data collection ST to be matchedC-1With meet condition
Each fusion track data collection (FTAAnd FTB) between similarity include:
The first step calculates sensor track data collection STC-1With merge track data collection FTAIn sensor track data
Collect STL1-ABetween similarity, be denoted as S1;
Second step calculates sensor track data collection STC-1With merge track data collection FTAIn sensor track data
Collect STL2-ABetween similarity, be denoted as S2;
Third step calculates the average value of S1 and S2, which is sensor track data collection STC-1With merge track
Data set FTABetween similarity;
4th step calculates sensor track data collection STC-1With merge track data collection FTBIn sensor track data
Collect STL3-BBetween similarity, be denoted as S3, due to merge track data collection FTBIn only include a sensor track data collection,
Therefore S3 is sensor track data collection STC-1With merge track data collection FTBBetween similarity.
Above-mentioned calculating process is summarized by formula are as follows: determine current sensor rail to be matched according to following formula
Any similarity merged between track data collection of mark data set and the condition that meets:
Wherein, similar_sensor_track_m indicates current sensor track data collection to be matched and meets condition
Any fusion track data collection between similarity, n indicate meet condition any fusion track data concentrate sensor
The number of track data collection, SmIndicate current sensor track data collection to be matched, SiIndicate any fusion rail for meeting condition
I-th of sensor track data collection in mark data set, similar (Si,Sm) indicate sensor track data collection SiWith sensor
Track data collection SmBetween similarity.
Further, described to calculate separately current sensor track data collection to be matched and merged with any of the condition that meets
The similarity between each sensor track data collection that track data collection includes, comprising:
Any fusion track number of characteristic and the condition that meets that current sensor track data to be matched is concentrated
According to the characteristic that any sensor track data that collection includes is concentrated, registration process is carried out according to timestamp and characteristic dimension;
Calculate the sub- similarity between the characteristic of alignment;
Rail is merged with any of the condition that meets according to the current sensor track data collection to be matched of each sub- similarity calculation
The similarity between any sensor track data collection that mark data set includes.
Wherein, the characteristic dimension specifically can be speed dimension or location dimension etc..
Specifically, according to following formula according to the current sensor track data collection to be matched of each sub- similarity calculation and completely
The similarity between any sensor track data collection that any fusion track data collection of sufficient condition includes:
Wherein, A indicates that current sensor track data collection to be matched, α indicate any fusion track number for meeting condition
According to any sensor track data collection that collection includes, similar (A, α) indicates sensor track data collection A and sensor track
Similarity between data set α, each sensor track data collection include the m frame sensing data continuously exported according to same sensor
The characteristic for the same object that frame determines, AiInclude in the i-th frame sensing data frame in expression sensor track data collection A
Object, αiIndicate the object for including in the i-th frame sensing data frame in sensor track data collection α, similar_object (Ai,
αi) indicate object AiWith object αiBetween similarity;
similar_object(Ai,αi) obtained according to following formula:
Wherein, similar_feature (Aij,αij) indicate object AiWith object αiJth dimensional feature data between son
Similarity, n indicate the dimension sum of object features data.
Further, sub- similarity (the i.e. above-mentioned object A between the characteristic for calculating alignmentiWith object αi?
Sub- similarity between j dimensional feature data), comprising:
The difference between the characteristic of alignment is calculated, searches the difference pair in the posterior probability mapping table pre-established
The posterior probability values are determined as the corresponding sub- similarity of characteristic of alignment by the posterior probability values answered;
Wherein, between the characteristic that the same object of different sensors output is preserved in the posterior probability mapping table
Difference range and posterior probability values between mapping relations.
Difference between the characteristic of the same object of the different sensors output saved in the posterior probability mapping table
Mapping relations between value range and posterior probability values are obtained by following test method:
During the test, it is assumed that the speed of object 1 is detected by the first camera and the second camera respectively,
The result of detection for the first time are as follows: the first camera detects that the speed of object 1 is 45m/s, and the second camera detects object 1
Speed is 43m/s, then the speed difference for the object 1 that the two detects is 45-43=2;The result of second of detection are as follows: first shines
Camera detects that the speed of object 1 is 46m/s, and the second camera detects that the speed of object 1 is 42m/s, then the two detects
Object 1 speed difference be 46-42=4;Repeated detection is carried out according to above-mentioned test method, it is assumed that has carried out 100 inspections altogether
It surveys, the difference of testing result falls in having 10 times between range 0-2, then the corresponding posterior probability values of difference range 0-2 are 10/
100=0.1.Above-mentioned posterior probability mapping table can be obtained according to above-mentioned test method.
Step 230 determines sensor track number to be matched using bipartite graph matching algorithm according to determining similarity
According to the incidence relation collected between corresponding object object corresponding with fusion track data collection.
Wherein, it is less than when some sensor track data collection to be matched merges the similarity between track data collection with certain
Given threshold, then some described sensor track data collection to be matched with it is described certain merge track data collection and be no longer participate in and be based on
Bipartite graph matching algorithm determines the corresponding object of sensor track data collection to be matched object corresponding with fusion track data collection
The operation of incidence relation between body.If some sensor track data collection to be matched merges track with all of the condition that meets
Similarity between data set is respectively less than given threshold, it is determined that there is no with some described sensor track data to be matched
Collect the fusion track data collection with incidence relation.
The technical solution of the present embodiment, on the basis of above-described embodiment technical solution, by calculate separately currently to
The sensor track data collection matched and the condition that meets it is any merge each sensor track data collection that track data collection includes it
Between similarity, and current sensor track data collection and any for the condition that meets to be matched are determined according to the similarity of calculating
The technological means for merging the similarity between track data collection improves current sensor track data collection to be matched and meets
The computational accuracy of similarity between any fusion track data collection of condition, and then improve the phase jljl from different sensors
Association accuracy and association efficiency between body.
Embodiment three
Fig. 3 is the flow diagram of the object correlating method of one of the embodiment of the present invention three multisensor, above-mentioned
On the basis of embodiment technical solution, the present embodiment advanced optimizes the object correlating method, referring specifically to Fig. 3
Shown, the object correlating method includes the following steps:
Step 310, at least one sensor track data collection to be matched for being currently generated of acquisition and saved to
A few fusion track data collection;Wherein, respectively fusion track data collection includes at least one existing sensor track data collection.
Wherein, each sensor track data collection includes the sensing data of the setting number continuously exported according to same sensor
The characteristic for the same object that frame determines respectively, object corresponding to different sensors track data collection is different, same fusion
The identical and corresponding sensor of object corresponding to each sensor track data collection that track data collection includes is different, and
Object corresponding to difference fusion track data collection is different.
Step 320, determine sensor track data collection to be matched with merge the similarity between track data collection.
Step 330, according to similarity definitive result, determine the corresponding object of sensor track data collection to be matched and melt
Close the incidence relation between the corresponding object of track data collection.
The sensor track data collection tool to be matched with this is added in sensor track data collection to be matched by step 340
Relevant fusion track data is concentrated;Alternatively,
There is no there is when merging track data collection of incidence relation with sensor track data collection to be matched, it is based on
Sensor track data collection to be matched generates new fusion track data collection.
Assuming that sensor track data to be matched integrates the sensor track data collection about object 1 obtained as camera
STC-1, integrate with its track data that merges with incidence relation as FTA, FTAIn comprising the first detections of radar arrive for object A
Sensor track data collection STL1-A, and the sensor track data collection ST for object A that the second detections of radar arrivesL2-A, then
By sensor track data collection STC-1Fusion track data collection FT is addedAIn, at this point, fusion track data collection FTAIt is passed including three
Sensor track data collection, is respectively as follows: STC-1、STL1-AAnd STL2-A, it is same according to object association results object 1 and object A
Object.
Step 350, when getting new sensing data frame, using the new sensing data frame to corresponding fusion track
Data set is updated.
Wherein, the new sensing data frame is that (can also be other settings based on 5 frames that certain sensor continuously exports
Number, the present embodiment are illustrated by taking 5 frames as an example) after the obtained sensor track data collection of sensing data frame, certain sensing
The sensing data frame that device is got again is the corresponding new sensing data frame of certain sensor.For example, in the example above, radar
In 5s, 5 frame sensing data frames of continuous output are gathered enough, have obtained the sensor track number of object A respectively at this time
According to collection STL-AAnd the sensor track data collection ST of object BL-B;And the sensing data frame that camera continuously exports just gathers in 6s
Enough 5 frames, therefore the sensor track data collection ST of object A that radar obtainsL-AWith the sensor track data collection ST of object BL-BPoint
The two fusion track data collection saved are not constituted, are denoted as fusion track data collection FT respectivelyAAnd FTB, based on camera the
The sensor track data collection ST for the object 1 that the 5 frame sensing data frames continuously exported gathered enough when 6s obtainC-1And object 2
Sensor track data integrates as STC-2For two sensor track data collection to be matched.Meanwhile in 6s, the radar is again
A frame sensing data frame can be exported, which is above-mentioned new sensing data frame, then utilizes the new sensing data
Frame is to the fusion track data collection FTAAnd FTBIt is updated.
Specifically: when getting new sensing data frame, the feature of object is determined according to the new sensing data frame
Data.For each fusion track data collection saved, judge the object determined whether with present fusion track data collection
Whether sensor corresponding to the identical and new sensing data frame of corresponding object is right with present fusion track data collection institute
The sensor answered is identical, if so, being updated according to the characteristic for the object determined to present fusion track data collection.
Further, the characteristic for the object that the basis is determined is updated present fusion track data collection,
Include:
Using the characteristic for the object determined, present fusion track data is concentrated and is passed with corresponding to this feature data
The corresponding sensor track data of sensor is concentrated, and the characteristic farthest apart from current time is replaced.
For example, the characteristic of the object A determined using the sensing data frame exported based on the radar 6s, replacement
Sensor track data collection STL-ADescribed in object A in the sensing data frame that is exported in 1s of radar the characteristic
According to.The characteristic can specifically refer to speed or the position etc. of object.
Step 360 determines whether updated fusion track data collection is effective, if effectively, thening follow the steps 370, retaining
Updated fusion track data collection, if in vain, thening follow the steps 380, being removed to updated fusion track data collection
Processing, so that fusion track data collection of removing that treated is effective or updated fusion track data collection is deleted.
Further, whether the updated fusion track data collection of the determination is effective, comprising:
If it includes a sensor track data collection that updated fusion track data, which is concentrated, it is determined that updated fusion
Track data concentrates the final updating time for the sensor track data collection for including, and is determined according to the final updating time updated
Whether effective merge track data collection;If final updating time gap current time is farther out, it is determined that updated fusion track
Data set is invalid.
If it includes multiple sensor track data collection that updated fusion track data, which is concentrated, by updated fusion rail
Each sensor track data collection in mark data set inputs the neural network model trained in advance, obtains the neural network mould
The updated fusion track data of type output concentrates each sensor track data collection correspond to the probability value of same object, if this is generally
Rate value is greater than given threshold, it is determined that updated fusion track data collection is effective, if the probability value is less than given threshold, counts
Total similarity that updated fusion track data concentrates each sensor track data collection is calculated, is determined according to total similarity after updating
Fusion track data collection it is whether effective.
Wherein, the neural network model trained in advance carries out learning training based on the training data of setting quantity,
Every group of training data includes at least two sensor track data collection, and each sensor track data collection carries object mark
Note, the sensor track data collection of corresponding same object carry identical object marker, the sensor rail of corresponding different objects
Mark data set carries different object markers.Neural network model is in learning training based on known corresponding same object
The sensor track data collection of sensor track data collection or known corresponding different objects carries out feature learning, each to remember
The feature of sensor track data collection reaches and identifies which sensor track data collection corresponds to identical object, which sensor
Track data collection corresponds to different objects, and can be calculated often by counting the object marker that each sensor track data collection carries
At least two sensor track data collection that group training data includes correspond to the probability of same object.Specifically, according to following public affairs
Formula calculates total similarity that updated fusion track data concentrates each sensor track data collection:
Wherein, similarity indicates total similarity, and n indicates updated fusion track data centralized sensors track number
According to the number of collection,For number of combinations formula, any 2 sensor track datas for concentrating n sensor track data are indicated
Collect the combination sum being combined, SiIndicate that updated fusion track data concentrates i-th of sensor track data collection, SjTable
Show that updated fusion track data concentrates j-th of sensor track data collection, simliar (Si,Sj) indicate sensor track number
According to collection SiWith sensor track data collection SjBetween similarity.
It is further, described that dismounting processing is carried out to updated fusion track data collection, comprising:
If it includes a sensor track data collection that updated fusion track data, which is concentrated, updated fusion is deleted
Track data collection;
If it includes two sensor track data collection that updated fusion track data, which is concentrated, according to setting redundant rule elimination
The sensor track data collection that updated fusion track data is concentrated, and continue judgement and delete sensor track data
Whether the fusion track data collection of collection operates effectively;The setting rule, which for example can be, deletes the minimum sensor of priority
Corresponding sensor track data collection or renewal time are apart from the farther away sensor track data collection of current time;
If it includes at least three sensor track data collection that updated fusion track data, which is concentrated, calculate separately at least
Three sensor track datas are concentrated total similar between each sensor track data collection and other sensors track data collection
The fusion track data of the minimum corresponding sensor track data collection of total similarity in the updated is concentrated and is deleted, and pressed by degree
Continue to judge whether the fusion track data collection for deleting sensor track data collection operates effectively according to the above method.
Specifically, calculating at least three sensor track datas according to following formula concentrates each sensor track data collection
With total similarity between other sensors track data collection:
Wherein, similar_sensor_track_m indicates total similarity, and n indicates that updated fusion track data is concentrated
The number for the sensor track data collection for including, SiIndicate i-th of sensor track that updated fusion track data is concentrated
Data set, SmIndicate current sensor track data collection to be calculated, similar (Si,Sm) indicate sensor track data collection Si
With sensor track data collection SmBetween similarity, i ≠ m.
Step 370 retains updated fusion track data collection.
Step 380 carries out dismounting processing to updated fusion track data collection, so as to remove, treated merges track
Data set is effective or updated fusion track data collection is deleted.
On the basis of above-described embodiment technical solution, the technical solution of the present embodiment is getting new sensing data
When frame, corresponding fusion track data collection is updated using the new sensing data frame, and further ensures that updated
The validity for merging track data collection, improves association accuracy and the association between the same object from different sensors
Efficiency.
Example IV
Fig. 4 is the structural schematic diagram of the object associated apparatus of one of the embodiment of the present invention four multisensor, referring to fig. 4
Shown, the object associated apparatus includes: to obtain module 410, similarity determining module 420 and incidence relation determining module 430;
Wherein, obtain module 410, for obtain at least one the sensor track data collection to be matched being currently generated with
And at least one fusion track data collection saved;Wherein, respectively fusion track data collection includes at least one existing sensing
Device track data collection;
Similarity determining module 420, for determine sensor track data collection to be matched with merge track data collection it
Between similarity;
Incidence relation determining module 430, for determining sensor track data to be matched according to similarity definitive result
Collect the incidence relation between corresponding object object corresponding with track data collection is merged;
Wherein, each sensor track data collection includes the sensing data of the setting number continuously exported according to same sensor
The characteristic for the same object that frame determines respectively, object corresponding to different sensors track data collection is different, same fusion
The identical and corresponding sensor of object corresponding to each sensor track data collection that track data collection includes is different, and
Object corresponding to difference fusion track data collection is different.
Further, similarity determining module 420 is specifically used for: for each sensor track data collection to be matched,
Current sensor track data collection to be matched and each similarity merged between track data collection for the condition that meets are determined respectively;
Sensor corresponding to each sensor track data collection that the fusion track data collection for meeting condition includes with currently to
Sensor corresponding to the sensor track data collection matched is different;
Corresponding, incidence relation determining module 430 is specifically used for: according to determining similarity, being calculated using bipartite graph matching
Method determines the association between the corresponding object of sensor track data collection to be matched object corresponding with fusion track data collection
Relationship.
Further, similarity determining module 420 is specifically used for: calculating separately current sensor track data to be matched
Collect any similarity merged between each sensor track data collection that track data collection includes with the condition that meets, according to calculating
Similarity determine that current sensor track data collection to be matched merges between track data collection with any of the condition that meets
Similarity.
Further, similarity determining module 420 is specifically used for: current sensor track data to be matched is concentrated
The characteristic that any sensor track data that characteristic and any fusion track data collection for meeting condition include is concentrated,
Registration process is carried out according to timestamp and characteristic dimension;
Calculate the sub- similarity between the characteristic of alignment;
Rail is merged with any of the condition that meets according to the current sensor track data collection to be matched of each sub- similarity calculation
The similarity between any sensor track data collection that mark data set includes.
Further, similarity determining module 420 is specifically used for: the difference between the characteristic of alignment is calculated, pre-
The corresponding posterior probability values of the difference are searched in the posterior probability mapping table first established, which is determined as alignment
The corresponding sub- similarity of characteristic;
Wherein, between the characteristic that the same object of different sensors output is preserved in the posterior probability mapping table
Difference range and posterior probability values between mapping relations.
Further, described device further include: processing module, for determining sensor track data collection pair to be matched
After incidence relation between the object answered object corresponding with fusion track data collection, by sensor track data to be matched
Collection, which is added, with the sensor track data collection to be matched there is the track data that merges of incidence relation to concentrate;Alternatively, not depositing
There is when merging track data collection of incidence relation with sensor track data collection to be matched, based on sensor to be matched
Track data collection generates new fusion track data collection.
Further, described device further include: update module, for when getting new sensing data frame, according to this
New sensing data frame determines the characteristic of object;For the fusion track data collection of each preservation, judge to determine
Object whether sensor corresponding to the sensing data frame identical and new as object corresponding to present fusion track data collection
It is whether identical as sensor corresponding to present fusion track data collection, if so, according to the characteristic for the object determined
Present fusion track data collection is updated.
Further, the update module is specifically used for: using the characteristic for the object determined, by present fusion rail
Sensor track data corresponding with sensor corresponding to this feature data is concentrated in mark data set, farthest apart from current time
Characteristic is replaced.
Further, described device further includes validity determining module, for carrying out to present fusion track data collection
After update, determine whether updated fusion track data collection is effective, if effectively, retaining updated fusion track data
Collection, if in vain, carrying out dismounting processing to updated fusion track data collection, so as to remove, treated merges track data
Collect effective or updated fusion track data collection to be deleted.
Further, the validity determining module is specifically used for:
If it includes a sensor track data collection that updated fusion track data, which is concentrated, it is determined that updated fusion
Track data concentrates the final updating time for the sensor track data collection for including, and is determined according to the final updating time updated
Whether effective merge track data collection;
If it includes multiple sensor track data collection that updated fusion track data, which is concentrated, by updated fusion rail
Each sensor track data collection in mark data set inputs the neural network model trained in advance, obtains the neural network mould
The updated fusion track data of type output concentrates each sensor track data collection correspond to the probability value of same object, if this is generally
Rate value is greater than given threshold, it is determined that updated fusion track data collection is effective, if the probability value is less than given threshold, counts
Total similarity that updated fusion track data concentrates each sensor track data collection is calculated, is determined according to total similarity after updating
Fusion track data collection it is whether effective.
Further, the validity determining module includes removing unit, and the dismounting unit is specifically used for:
If it includes a sensor track data collection that updated fusion track data, which is concentrated, updated fusion is deleted
Track data collection;
If it includes two sensor track data collection that updated fusion track data, which is concentrated, according to setting redundant rule elimination
The sensor track data collection that updated fusion track data is concentrated, and continue judgement and delete sensor track data
Whether the fusion track data collection of collection operates effectively;
If it includes at least three sensor track data collection that updated fusion track data, which is concentrated, calculate separately at least
Three sensor track datas are concentrated total similar between each sensor track data collection and other sensors track data collection
The fusion track data of the minimum corresponding sensor track data collection of total similarity in the updated is concentrated and is deleted by degree, and after
It is continuous to judge whether the fusion track data collection for deleting sensor track data collection operates effectively.
The technical solution of the present embodiment, improve association accuracy between the same object from different sensors and
It is associated with efficiency.
The object associated apparatus of multisensor provided by the embodiment of the present invention can be performed any embodiment of that present invention and be mentioned
The object correlating method of the multisensor of confession has the corresponding functional module of execution method and beneficial effect.
Embodiment five
Fig. 5 is the structural schematic diagram for a kind of electronic equipment that the embodiment of the present invention five provides.Fig. 5, which is shown, to be suitable for being used in fact
The block diagram of the example devices 12 of existing embodiment of the present invention.The equipment 12 that Fig. 5 is shown is only an example, should not be to this hair
The function and use scope of bright embodiment bring any restrictions.
As shown in figure 5, equipment 12 is showed in the form of universal computing device.The component of equipment 12 may include but unlimited
In one or more processor or processing unit 16, system storage 28, connecting different system components, (including system is deposited
Reservoir 28 and processing unit 16) bus 18.
Bus 18 indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.It lifts
For example, these architectures include but is not limited to industry standard architecture (ISA) bus, microchannel architecture (MAC)
Bus, enhanced isa bus, Video Electronics Standards Association (VESA) local bus and peripheral component interconnection (PCI) bus.
Equipment 12 typically comprises a variety of computer system readable media.These media can be it is any can be by equipment 12
The usable medium of access, including volatile and non-volatile media, moveable and immovable medium.
System storage 28 may include the computer system readable media of form of volatile memory, such as arbitrary access
Memory (RAM) 30 and/or cache memory 32.Equipment 12 may further include it is other it is removable/nonremovable,
Volatile/non-volatile computer system storage medium.Only as an example, storage system 34 can be used for reading and writing irremovable
, non-volatile magnetic media (Fig. 5 do not show, commonly referred to as " hard disk drive ").Although being not shown in Fig. 5, use can be provided
In the disc driver read and write to removable non-volatile magnetic disk (such as " floppy disk "), and to removable anonvolatile optical disk
The CD drive of (such as CD-ROM, DVD-ROM or other optical mediums) read-write.In these cases, each driver can
To be connected by one or more data media interfaces with bus 18.Memory 28 may include at least one program product,
The program product has one group of (such as obtaining module 410, similarity determining module 420 and incidence relation determining module 430) journey
Sequence module, these program modules are configured to perform the function of various embodiments of the present invention.
With one group of (obtaining module 410, similarity determining module 420 and incidence relation determining module 430) program module
42 program/utility 40, can store in such as memory 28, and such program module 42 includes but is not limited to operate
System, one or more application program, other program modules and program data, each of these examples or certain group
It may include the realization of network environment in conjunction.Program module 42 usually execute function in embodiment described in the invention and/
Or method.
Equipment 12 can also be communicated with one or more external equipments 14 (such as keyboard, sensing equipment, display 24 etc.),
Can also be enabled a user to one or more equipment interacted with the equipment 12 communication, and/or with enable the equipment 12 with
One or more of the other any equipment (such as network interface card, modem etc.) communication for calculating equipment and being communicated.It is this logical
Letter can be carried out by input/output (I/O) interface 22.Also, equipment 12 can also by network adapter 20 and one or
The multiple networks of person (such as local area network (LAN), wide area network (WAN) and/or public network, such as internet) communication.As shown,
Network adapter 20 is communicated by bus 18 with other modules of equipment 12.It should be understood that although not shown in the drawings, can combine
Equipment 12 use other hardware and/or software module, including but not limited to: microcode, device driver, redundant processing unit,
External disk drive array, RAID system, tape drive and data backup storage system etc..
Processing unit 16 by the program that is stored in system storage 28 of operation, thereby executing various function application and
Data processing, such as realize the object correlating method of multisensor provided by the embodiment of the present invention.
Embodiment six
The embodiment of the present invention six additionally provides a kind of computer readable storage medium, is stored thereon with computer program, should
The object correlating method of the multisensor as described in any embodiment of the present invention is realized when program is executed by processor.
The computer storage medium of the embodiment of the present invention, can be using any of one or more computer-readable media
Combination.Computer-readable medium can be computer-readable signal media or computer readable storage medium.It is computer-readable
Storage medium for example may be-but not limited to-the system of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, device or
Device, or any above combination.The more specific example (non exhaustive list) of computer readable storage medium includes: tool
There are electrical connection, the portable computer diskette, hard disk, random access memory (RAM), read-only memory of one or more conducting wires
(ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-
ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer-readable storage
Medium can be any tangible medium for including or store program, which can be commanded execution system, device or device
Using or it is in connection.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for
By the use of instruction execution system, device or device or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited
In wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
The computer for executing operation of the present invention can be write with one or more programming languages or combinations thereof
Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++,
It further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with
It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion
Divide and partially executes or executed on a remote computer or server completely on the remote computer on the user computer.?
Be related in the situation of remote computer, remote computer can pass through the network of any kind --- including local area network (LAN) or
Wide area network (WAN)-be connected to subscriber computer, or, it may be connected to outer computer (such as mentioned using Internet service
It is connected for quotient by internet).
Claims (14)
1. a kind of object correlating method of multisensor characterized by comprising
At least one the fusion rail for obtaining at least one the sensor track data collection to be matched being currently generated and having saved
Mark data set;Wherein, respectively fusion track data collection includes at least one existing sensor track data collection;
Determine sensor track data collection to be matched with merge the similarity between track data collection;
According to similarity definitive result, determine the corresponding object of sensor track data collection to be matched with merge track data collection
Incidence relation between corresponding object;
Wherein, each sensor track data collection includes the sensing data frame point of the setting number continuously exported according to same sensor
Not Que Ding same object characteristic, object corresponding to different sensors track data collection is different, same fusion track
The identical and corresponding sensor of object corresponding to each sensor track data collection that data set includes is different, and different
It is different to merge object corresponding to track data collection.
2. object correlating method according to claim 1, which is characterized in that determination sensor track number to be matched
Include: with the similarity merged between track data collection according to collection
For each sensor track data collection to be matched, current sensor track data collection to be matched is determined respectively and is expired
Similarity between each fusion track data collection of sufficient condition;Each sensing that the fusion track data collection for meeting condition includes
Sensor corresponding to device track data collection is different from sensor corresponding to current sensor track data collection to be matched;
It is corresponding, it is described according to similarity definitive result, it determines the corresponding object of sensor track data collection to be matched and melts
Close the incidence relation between the corresponding object of track data collection, comprising:
The corresponding object of sensor track data collection to be matched is determined using bipartite graph matching algorithm according to determining similarity
Incidence relation between body object corresponding with fusion track data collection.
3. object correlating method according to claim 2, which is characterized in that the current sensor rail to be matched of the determination
Each similarity merged between track data collection of mark data set and the condition that meets, comprising:
Calculate separately current sensor track data collection to be matched includes with any track data collection that merges for the condition that meets
Similarity between each sensor track data collection determines current sensor track data to be matched according to the similarity of calculating
Collect any similarity merged between track data collection with the condition that meets.
4. object correlating method according to claim 3, which is characterized in that described to calculate separately current sensing to be matched
Device track data collection and the condition that meets it is any merge it is similar between each sensor track data collection that track data collection includes
Degree, comprising:
Any fusion track data collection of characteristic and the condition that meets that current sensor track data to be matched is concentrated
The characteristic that any sensor track data for including is concentrated carries out registration process according to timestamp and characteristic dimension;
Calculate the sub- similarity between the characteristic of alignment;
Track number is merged with any of the condition that meets according to the current sensor track data collection to be matched of each sub- similarity calculation
The similarity between any sensor track data collection for including according to collection.
5. object correlating method according to claim 4, which is characterized in that between the characteristic for calculating alignment
Sub- similarity, comprising:
The difference between the characteristic of alignment is calculated, it is corresponding to search the difference in the posterior probability mapping table pre-established
The posterior probability values are determined as the corresponding sub- similarity of characteristic of alignment by posterior probability values;
Wherein, the difference between the characteristic of the same object of different sensors output is preserved in the posterior probability mapping table
The mapping relations being worth between range and posterior probability values.
6. object correlating method according to claim 1, which is characterized in that determine sensor track data collection to be matched
After incidence relation between corresponding object object corresponding with fusion track data collection, the method also includes:
Sensor track data collection to be matched, which is added, has incidence relation with the sensor track data collection to be matched
Track data is merged to concentrate;Alternatively,
There is no with sensor track data collection to be matched have when merging track data collection of incidence relation, based on to
The sensor track data collection matched generates new fusion track data collection.
7. object correlating method according to claim 1, which is characterized in that further include:
When getting new sensing data frame, the characteristic of object is determined according to the new sensing data frame;
For the fusion track data collection of each preservation, judge whether the object determined is right with present fusion track data collection institute
Sensor corresponding to the identical and new sensing data frame of the object answered whether with corresponding to present fusion track data collection
Sensor is identical, if so, being updated according to the characteristic for the object determined to present fusion track data collection.
8. object correlating method according to claim 7, which is characterized in that the characteristic for the object that the basis is determined
It is updated according to present fusion track data collection, comprising:
Using the characteristic for the object determined, present fusion track data is concentrated and sensor corresponding to this feature data
Corresponding sensor track data is concentrated, and the characteristic farthest apart from current time is replaced.
9. object correlating method according to claim 7, which is characterized in that carried out more to present fusion track data collection
After new, further includes:
Determine whether updated fusion track data collection is effective, if effectively, retaining updated fusion track data collection, if
In vain, then dismounting processing is carried out to updated fusion track data collection so that remove treated fusion track data collection have
Effect or updated fusion track data collection are deleted.
10. object correlating method according to claim 9, which is characterized in that the updated fusion track number of determination
It is whether effective according to collecting, comprising:
If it includes a sensor track data collection that updated fusion track data, which is concentrated, it is determined that updated fusion track
The final updating time for the sensor track data collection for including in data set determines updated fusion according to the final updating time
Whether track data collection is effective;
If it includes multiple sensor track data collection that updated fusion track data, which is concentrated, by updated fusion track number
The neural network model trained in advance is inputted according to each sensor track data collection of concentration, it is defeated to obtain the neural network model
Updated fusion track data out concentrates each sensor track data collection to correspond to the probability value of same object, if the probability value
Greater than given threshold, it is determined that updated fusion track data collection is effective, if the probability value is less than given threshold, calculates more
Fusion track data after new concentrates total similarity of each sensor track data collection, determines updated melt according to total similarity
Whether effective close track data collection.
11. object correlating method according to claim 9, which is characterized in that described to updated fusion track data
Collection carries out dismounting processing, comprising:
If it includes a sensor track data collection that updated fusion track data, which is concentrated, updated fusion track is deleted
Data set;
If it includes two sensor track data collection that updated fusion track data, which is concentrated, updated according to setting redundant rule elimination
The sensor track data collection that fusion track data afterwards is concentrated, and continue judgement and delete sensor track data collection
Whether fusion track data collection operates effectively;
If it includes at least three sensor track data collection that updated fusion track data, which is concentrated, at least three are calculated separately
Sensor track data concentrates total similarity between each sensor track data collection and other sensors track data collection, will
The fusion track data of the corresponding sensor track data collection of minimum total similarity in the updated, which is concentrated, to be deleted, and continues to judge
Whether the fusion track data collection for deleting sensor track data collection operates effectively.
12. a kind of object associated apparatus of multisensor characterized by comprising
Obtain module, for obtain at least one the sensor track data collection to be matched being currently generated and saved to
A few fusion track data collection;Wherein, respectively fusion track data collection includes at least one existing sensor track data collection;
Similarity determining module, for determine sensor track data collection to be matched with merge it is similar between track data collection
Degree;
Incidence relation determining module, for determining that sensor track data collection to be matched is corresponding according to similarity definitive result
Object and the corresponding object of fusion track data collection between incidence relation;
Wherein, each sensor track data collection includes the sensing data frame point of the setting number continuously exported according to same sensor
Not Que Ding same object characteristic, object corresponding to different sensors track data collection is different, same fusion track
The identical and corresponding sensor of object corresponding to each sensor track data collection that data set includes is different, and different
It is different to merge object corresponding to track data collection.
13. a kind of electronic equipment, which is characterized in that the electronic equipment includes:
One or more processors;
Storage device, for storing 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 are real
The now object correlating method of the multisensor as described in any in claim 1-11.
14. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
The object correlating method of the multisensor as described in any in claim 1-11 is realized when execution.
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