CN113449624B - Method and device for determining vehicle behavior based on pedestrian re-identification - Google Patents
Method and device for determining vehicle behavior based on pedestrian re-identification Download PDFInfo
- Publication number
- CN113449624B CN113449624B CN202110689288.8A CN202110689288A CN113449624B CN 113449624 B CN113449624 B CN 113449624B CN 202110689288 A CN202110689288 A CN 202110689288A CN 113449624 B CN113449624 B CN 113449624B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- determining
- image
- distance
- image set
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 230000008859 change Effects 0.000 claims abstract description 57
- 238000012544 monitoring process Methods 0.000 claims abstract description 17
- 230000009471 action Effects 0.000 claims description 25
- 238000001514 detection method Methods 0.000 claims description 11
- 238000000605 extraction Methods 0.000 claims description 9
- 230000036544 posture Effects 0.000 claims description 7
- 230000009191 jumping Effects 0.000 claims description 6
- 230000006399 behavior Effects 0.000 description 21
- 230000006870 function Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
- G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
- G07B15/02—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points taking into account a variable factor such as distance or time, e.g. for passenger transport, parking systems or car rental systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30196—Human being; Person
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30241—Trajectory
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Business, Economics & Management (AREA)
- Finance (AREA)
- Traffic Control Systems (AREA)
Abstract
The embodiment of the invention provides a method and a device for determining vehicle behavior based on pedestrian re-identification, wherein the method comprises the following steps: acquiring an image of a preset monitoring area, identifying a current image, and judging whether a vehicle exists in the current image; if the vehicle is in the moving state, acquiring a first image set containing the vehicle in the moving state in a preset time period, and determining the portrait information of the driver of the vehicle through a pedestrian re-identification algorithm; determining a first distance variation trend of the distance between the driver and the vehicle and a second distance variation trend of the distance between the vehicle and the berth; and determining the in-out behavior of the vehicle according to the first distance change trend and the second distance change trend. The invention realizes that the access behavior of the vehicle can be accurately determined without being limited by the license plate number, and avoids the situation that the vehicle behavior cannot be determined because the vehicle image cannot be acquired due to the limitation of factors such as the limitation of the shooting angle of the monitoring camera, the environment and the like.
Description
Technical Field
The invention relates to the technical field of intelligent parking management, in particular to a method and a device for determining vehicle behaviors based on pedestrian re-identification.
Background
Today, the economic development is rapid, the living standard and income of people are continuously improved, the maintenance amount of urban motor vehicles is rapidly increased, the gaps of urban parking spaces are continuously enlarged, huge parking demands can not be met far, and the contradiction between the parking spaces and the parking demands is increasingly sharp. Especially on two sides of an urban road, due to the scarcity of road side parking spaces and the light traffic safety awareness of motor vehicle drivers, urban road side parking and road side illegal parking become one of the urban management diseases, so that the problems of traffic jam and the like seriously restrict urban green and rapid development, seriously influence urban appearance and resident living environment, and are in an unprecedented degree for the management of urban road side parking and road side illegal parking.
Along with the maturity of high-order video technique, real-time automatic supervision road side parking area has become the main mode of road side parking management, but owing to receive the restriction of factors such as on-the-spot construction, environment, can appear that some surveillance cameras exist and can't monitor the condition of pipeline side parking action, simultaneously, also appear the target vehicle frequently and shelter from by other large-scale vehicles and lead to unable condition of supervision yet, in addition, current high-order video technique supervision pipeline side parking action often relies on discernment target vehicle's license plate number information, but because the problem of surveillance camera shooting angle limitation still exists when target vehicle parks into berth, can not clearly catch the condition of target vehicle license plate number. Therefore, how to accurately determine the vehicle behavior without being limited by the license plate number becomes a challenge to be solved.
Disclosure of Invention
The embodiment of the invention provides a method and a device for determining vehicle behaviors based on pedestrian re-identification, which realize that the vehicle behaviors can be accurately determined without being limited by license plate numbers.
In one aspect, an embodiment of the present invention provides a method for determining a vehicle behavior based on pedestrian re-recognition, including:
acquiring an image of a preset monitoring area, identifying a current image, and judging whether a vehicle exists in the current image;
If the vehicle is in the moving state, a first image set containing the vehicle in a preset time period is obtained, the portrait information in the first image set is determined through a pedestrian re-identification algorithm, and the portrait information of the driver of the vehicle is determined according to the portrait information in the first image set;
Determining the action track of the driver and the position of the berth where the vehicle is located in the first image set;
Determining a first distance change trend of the distance between the driver and the vehicle and a second distance change trend of the distance between the vehicle and the berth according to the action track of the driver and the position of the berth where the vehicle is located;
And determining the in-out behavior of the vehicle according to the first distance change trend and the second distance change trend.
Further, the acquiring a first image set including the vehicle in a moving state within a predetermined period of time includes:
step A, continuously acquiring a second image set of a preset monitoring area in a preset time period, and judging whether the second image set contains images of a vehicle in a moving state or not;
if not, jumping to the step A until the second image set contains the images of the vehicle in a moving state;
and if so, determining the second image set as a first image set.
Further, the determining, by the pedestrian re-recognition algorithm, the portrait information in the first image set includes:
According to a preset characteristic extraction network model, determining the characteristics of the figures in each moving image of the vehicle in a moving state in the first image set;
calculating the feature distance in each moving image according to the features;
and sequencing the characteristic distances in each moving image, and determining the portrait information in the vehicle from each moving image.
Further, the determining the portrait information of the driver of the vehicle according to the portrait information in the first image set includes:
According to the portrait information, portrait information in a driving state at the driving position of the vehicle is determined;
detecting a position rectangular frame of the portrait at the driving position of the vehicle in each moving image through a preset feature extraction network model;
Dividing each moving image according to the pixel probability of the portrait in each moving image based on the position rectangular frame and aiming at the portrait of the driving position of the vehicle;
Performing background replacement on each segmented moving image to obtain segmented images of each moving image;
and detecting skeleton key points and postures of the portraits of the driving positions of the vehicles in the divided images, and determining the portraits information of the drivers of the vehicles in the first image set according to detection results.
Further, the determining, in the first image set, the action track of the driver and the position of the berth where the vehicle is located includes:
Determining the characteristics of the driver in each moving image of the vehicle in a static state in the first image set according to the determined portrait information of the driver of the vehicle;
and determining the action track of the driver according to the characteristics.
Further, the determining a first distance variation trend of the distance between the driver and the vehicle and a second distance variation trend of the distance between the vehicle and the berth according to the action track of the driver and the position of the berth where the vehicle is located includes:
determining a first change trend of the distance between the driver and the vehicle according to the action track of the driver;
And determining a second distance change trend of the distance between the vehicle and the berth in the first image set through a preset image recognition algorithm according to the determined position of the berth where the vehicle is located.
Further, the determining the in-out behavior of the vehicle according to the first distance variation trend and the second distance variation trend includes:
If the first distance change trend is gradually reduced and the second distance change trend is gradually increased, determining that the vehicle leaves;
and if the first distance change trend is gradually increased and the second distance change trend is unchanged, determining that the vehicle enters the ground.
In another aspect, an embodiment of the present invention provides an apparatus for determining a behavior of a vehicle based on pedestrian re-recognition, including:
the acquisition and judgment module is used for acquiring an image of a preset monitoring area, identifying a current image and judging whether a vehicle exists in the current image;
The acquisition and determination module is used for acquiring a first image set containing the vehicle in a moving state in a preset time period, determining the portrait information in the first image set through a pedestrian re-identification algorithm, and determining the portrait information of a driver of the vehicle according to the portrait information in the first image set;
The first determining module is used for determining the action track of the driver and the position of the berth where the vehicle is located in the first image set;
The second determining module is used for determining a first distance change trend of the distance between the driver and the vehicle and a second distance change trend of the distance between the vehicle and the berth according to the action track of the driver and the position of the berth where the vehicle is located;
And the third determining module is used for determining the in-out behavior of the vehicle according to the first distance change trend and the second distance change trend.
Further, the acquiring and determining module includes:
A judging unit, configured to continuously acquire a second image set of a predetermined monitoring area within a predetermined period of time, and judge whether the second image set contains an image of a vehicle in a moving state;
a jumping unit, configured to jump to the judging unit if the second image set does not include an image in which the vehicle is in a moving state;
and the first determining unit is used for determining the second image set as the first image set if the second image set is included.
Further, the acquiring and determining module includes:
A second determining unit configured to extract a network model according to a predetermined feature, and determine, in the first image set, a feature of a portrait in each moving image in which the vehicle is in a moving state;
a calculating unit for calculating a feature distance in each moving image according to the feature;
And the sorting unit is used for sorting the characteristic distances in the moving images and determining the portrait information in the vehicle from the moving images.
Further, the acquiring and determining module includes:
A third determining unit, configured to determine, according to the portrait information, portrait information in a driving state at the driving position of the vehicle;
A detection unit for detecting a position rectangular frame of a portrait in the vehicle driving position in each moving image through a predetermined feature extraction network model;
A dividing unit configured to divide each moving image for the portrait of the vehicle driving position according to the pixel probability of the portrait in each moving image based on the position rectangular frame;
a replacing unit, configured to perform background replacement on each of the segmented moving images to obtain segmented images of each of the moving images;
and the detection and determination unit is used for detecting skeleton key points and postures of the portraits of the driving positions of the vehicles in the divided images and determining portraits information of the drivers of the vehicles in the first image set according to detection results.
Further, the first determining module is specifically configured to
Determining the characteristics of the driver in each moving image of the vehicle in a static state in the first image set according to the determined portrait information of the driver of the vehicle;
and determining the action track of the driver according to the characteristics.
Further, the second determining module is specifically configured to
Determining a first change trend of the distance between the driver and the vehicle according to the action track of the driver;
And determining a second distance change trend of the distance between the vehicle and the berth in the first image set through a preset image recognition algorithm according to the determined position of the berth where the vehicle is located.
Further, the third determining module is specifically configured to
If the first distance change trend is gradually reduced and the second distance change trend is gradually increased, determining that the vehicle leaves;
and if the first distance change trend is gradually increased and the second distance change trend is unchanged, determining that the vehicle enters the ground.
The technical scheme has the following beneficial effects: according to the invention, the portrait information of the driver can be accurately determined based on the pedestrian re-recognition algorithm, and the entrance and exit behaviors of the vehicle can be accurately determined according to the position relation between the driver and the driving vehicle without being limited by license plate numbers, so that the situation that the vehicle behaviors cannot be determined due to the fact that the vehicle images cannot be acquired due to the limitation of factors such as the limitation of shooting angles of monitoring cameras and environment is avoided, further, important precondition guarantee is provided for the follow-up accurate and efficient road side parking management of the vehicle, and the use experience of users is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for determining vehicle behavior based on pedestrian re-identification in an embodiment of the invention;
Fig. 2 is a schematic structural diagram of an apparatus for determining vehicle behavior based on pedestrian re-recognition in another embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The technical scheme provided by the embodiment of the invention has the following beneficial effects: according to the invention, the portrait information of the driver can be accurately determined based on the pedestrian re-recognition algorithm, and the entrance and exit behaviors of the vehicle can be accurately determined according to the position relation between the driver and the driving vehicle without being limited by license plate numbers, so that the situation that the vehicle behaviors cannot be determined due to the fact that the vehicle images cannot be acquired due to the limitation of factors such as the limitation of shooting angles of monitoring cameras and environment is avoided, further, important precondition guarantee is provided for the follow-up accurate and efficient road side parking management of the vehicle, and the use experience of users is greatly improved.
The following describes the above technical solution of the embodiment of the present invention in detail with reference to an application example:
the application example of the invention aims to realize that the vehicle behavior can be accurately determined without being limited by license plate numbers.
In one possible implementation manner, in the road side parking management system, an image of a predetermined area is shot through a camera, an image of a predetermined monitoring area shot by the camera is acquired in real time, then the acquired current image is identified, whether a vehicle exists in the current image is judged, if the vehicle exists in the current image, such as the vehicle C, a first image set containing the vehicle C in a moving state in a predetermined time period is acquired, the portrait information in the first image set is determined through a pedestrian re-identification algorithm, and the portrait information of a driver of the vehicle C, such as the portrait information of the driver D, is determined according to the portrait information in the first image set; determining the action track of a driver D and the position of a berth where a vehicle C is located in a first image set; determining a first distance change trend of the distance between the driver D and the vehicle C and a second distance change trend of the distance between the vehicle C and the berth according to the action track of the driver D and the position of the berth where the vehicle C is located; and finally, determining the in-out behavior of the vehicle C according to the first distance change trend and the second distance change trend.
It should be noted that, through this embodiment, can not receive the restriction of license plate number, can confirm the vehicle behavior accurately, follow-up to the vehicle to carry out the in-process of roadside parking management, according to the clear image of arbitrary license plate of this vehicle in the image, behind the license plate number of confirming, can need not to receive the restriction of factors such as monitoring camera shooting angle limitation, environment, carries out the roadside parking management to the vehicle accurately high-efficient, like vehicle parking charge etc..
In a possible implementation manner, the step of acquiring the first image set including the vehicle in a moving state in the predetermined period in step 102 includes: step A, continuously acquiring a second image set of a preset monitoring area in a preset time period, and judging whether the second image set contains images of a vehicle in a moving state or not; if not, jumping to the step A until the second image set contains the images of the vehicle in a moving state; and if so, determining the second image set as a first image set.
For example, in the roadside parking management system, an image of a predetermined monitoring area captured by a camera is acquired in real time, the acquired current image is identified, whether a vehicle exists in the current image is determined, and if the vehicle exists in the current image, for example, a vehicle C, step a is executed: continuously acquiring a second image set of the predetermined monitoring area within a predetermined period of time, such as 5 minutes, if the acquisition time of the current image is 2021:08:00:10, the predetermined period of time is 2021:08:00:11 to 2021:08:05:10, and judging whether the second image set contains images of the vehicle C in a moving state or not; and C, if the judgment result is not included, jumping to the step A until the second image set contains the image of the vehicle C in the moving state, and when the second image set contains the image of the vehicle C in the moving state, re-determining the second image set as the first image set.
In one possible implementation, the step of determining, in step 102, the portrait information in the first image set through a pedestrian re-recognition algorithm includes: according to a preset characteristic extraction network model, determining the characteristics of the figures in each moving image of the vehicle in a moving state in the first image set; calculating the feature distance in each moving image according to the features; and sequencing the characteristic distances in each moving image, and determining the portrait information in the vehicle from each moving image.
For example, in the above example, in the roadside parking management system, a network model, such as a pre-trained pedestrian re-recognition-based feature network model, is extracted according to predetermined features, and in the first image set, features of figures in each moving image in which the vehicle C is in a moving state are determined; calculating the feature distance in each moving image according to the extracted portrait features; the feature distances in each moving image are sorted to obtain an average precision value, and then, according to the average precision value, the portrait information in the vehicle C is determined from each moving image, wherein the portrait information comprises the front face information, the hand gesture, the position in the vehicle and the like of the portrait.
Those skilled in the art will appreciate that Person re-identification (Person re-identification), also known as pedestrian re-identification, is a technique that uses computer vision techniques to determine whether a particular pedestrian is present in an image or video sequence. Widely recognized as a sub-problem of image retrieval. Given a monitored pedestrian image, the pedestrian image is retrieved across devices. The method aims to make up for the visual limitation of a fixed camera and can be combined with pedestrian detection/pedestrian tracking technology. In the embodiments of the present invention, specific algorithms will be described as examples, but the present invention is not limited thereto.
In a possible implementation manner, the step of determining, in step 102, the portrait information of the driver of the vehicle according to the portrait information in the first image set includes: according to the portrait information, portrait information in a driving state at the driving position of the vehicle is determined; detecting a position rectangular frame of the portrait at the driving position of the vehicle in each moving image through a preset feature extraction network model; dividing each moving image according to the pixel probability of the portrait in each moving image based on the position rectangular frame and aiming at the portrait of the driving position of the vehicle; performing background replacement on each segmented moving image to obtain segmented images of each moving image; and detecting skeleton key points and postures of the portraits of the driving positions of the vehicles in the divided images, and determining the portraits information of the drivers of the vehicles in the first image set according to detection results.
For example, in the road side parking management system, the person image information in the driving state at the driving position of the vehicle C is determined based on the extracted person image information, then, the position rectangular frames of the person images in the driving position of the vehicle C in each moving image are detected through a predetermined feature extraction network model, each moving image is segmented based on each position rectangular frame according to the pixel probability of the person images in each moving image, and background replacement is performed on each segmented moving image to obtain segmented images of each moving image; and detecting skeleton key points and postures of the portraits of the driving positions of the vehicles C in the divided images, determining the drivers D of the vehicles C in the first image set according to detection results, and determining the portraits information of the drivers D.
In a possible implementation manner, step 103 determines, in the first image set, a movement track of the driver and a position of a berth where the vehicle is located, including: determining the characteristics of the driver in each moving image of the vehicle in a static state in the first image set according to the determined portrait information of the driver of the vehicle; and determining the action track of the driver according to the characteristics.
For example, in the above example, in the roadside parking management system, the characteristics of the driver D, such as the posture characteristics, the movement characteristics, and the like of the driver, in each of the moving images in which the vehicle is in a stationary state are determined in the first image set based on the determined portrait information of the driver D of the vehicle C; and determining the action track of the driver D according to the gesture features and action features of the driver D.
In a possible implementation manner, step 104 of determining a first distance variation trend of the distance between the driver and the vehicle and a second distance variation trend of the distance between the vehicle and the berth according to the action track of the driver and the position of the berth where the vehicle is located includes: determining a first change trend of the distance between the driver and the vehicle according to the action track of the driver; and determining a second distance change trend of the distance between the vehicle and the berth in the first image set through a preset image recognition algorithm according to the determined position of the berth where the vehicle is located.
Wherein determining the access behavior of the vehicle according to the first distance variation trend and the second distance variation trend comprises: if the first distance change trend is gradually reduced and the second distance change trend is gradually increased, determining that the vehicle leaves; and if the first distance change trend is gradually increased and the second distance change trend is unchanged, determining that the vehicle enters the ground.
For example, in the above example, in the roadside parking management system, the first trend of change of the distance between the driver D and the vehicle C is determined to be gradually decreasing according to the movement track of the driver D, and the second trend of change of the distance between the vehicle C and the vehicle C is determined to be gradually increasing in the first image set according to the determined position of the vehicle C at the berth, by a predetermined image recognition algorithm, the departure of the vehicle C can be determined.
For example, in the road side parking management system, the first trend of change of the distance between the driver D and the vehicle C is determined to be gradually decreasing according to the movement track of the driver D, the second trend of change of the distance between the vehicle C and the vehicle C is determined to be gradually increasing according to the determined position of the vehicle C in the first image set by the predetermined image recognition algorithm, and the second trend of change of the distance between the vehicle C and the vehicle C is determined to be unchanged according to the determined position of the vehicle C in the first image set by the predetermined image recognition algorithm.
The embodiment of the invention provides a device for determining vehicle behavior based on pedestrian re-identification, which can realize the method embodiment provided above, and specific function implementation is shown in the description of the method embodiment and is not repeated herein.
It should be understood that the specific order or hierarchy of steps in the processes disclosed are examples of exemplary approaches. Based on design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate preferred embodiment of this invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. As will be apparent to those skilled in the art; various modifications to these embodiments will be readily apparent, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, as used in the specification or claims, the term "comprising" is intended to be inclusive in a manner similar to the term "comprising," as interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean "non-exclusive or".
Those of skill in the art will further appreciate that the various illustrative logical blocks (illustrative logical block), units, and steps described in connection with the embodiments of the invention may be implemented by electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software (interchangeability), various illustrative components described above (illustrative components), elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation is not to be understood as beyond the scope of the embodiments of the present invention.
The various illustrative logical blocks or units described in the embodiments of the invention may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described. A general purpose processor may be a microprocessor, but in the alternative, the general purpose processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. In an example, a storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may reside in a user terminal. In the alternative, the processor and the storage medium may reside as distinct components in a user terminal.
In one or more exemplary designs, the above-described functions of embodiments of the present invention may be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, the functions may be stored on a computer-readable medium or transmitted as one or more instructions or code on the computer-readable medium. Computer readable media includes both computer storage media and communication media that facilitate transfer of computer programs from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, such computer-readable media may include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to carry or store program code in the form of instructions or data structures and other data structures that may be read by a general or special purpose computer, or a general or special purpose processor. Further, any connection is properly termed a computer-readable medium, e.g., if the software is transmitted from a website, server, or other remote source via a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wireless such as infrared, radio, and microwave, and is also included in the definition of computer-readable medium. The disks (disks) and disks (disks) include compact disks, laser disks, optical disks, DVDs, floppy disks, and blu-ray discs where disks usually reproduce data magnetically, while disks usually reproduce data optically with lasers. Combinations of the above may also be included within the computer-readable media.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (6)
1. A method of determining vehicle behavior based on pedestrian re-identification, comprising:
acquiring an image of a preset monitoring area, identifying a current image, and judging whether a vehicle exists in the current image;
If the vehicle is in the moving state, a first image set containing the vehicle in a preset time period is obtained, the portrait information in the first image set is determined through a pedestrian re-identification algorithm, and the portrait information of the driver of the vehicle is determined according to the portrait information in the first image set;
the determining, by the pedestrian re-recognition algorithm, the portrait information in the first image set includes:
According to a preset characteristic extraction network model, determining the characteristics of the figures in each moving image of the vehicle in a moving state in the first image set;
calculating the feature distance in each moving image according to the features;
Sorting the feature distances in the moving images, and determining the portrait information in the vehicle from the moving images;
The determining the portrait information of the driver of the vehicle according to the portrait information in the first image set includes:
According to the portrait information, portrait information in a driving state at the driving position of the vehicle is determined;
detecting a position rectangular frame of the portrait at the driving position of the vehicle in each moving image through a preset feature extraction network model;
Dividing each moving image according to the pixel probability of the portrait in each moving image based on the position rectangular frame and aiming at the portrait of the driving position of the vehicle;
Performing background replacement on each segmented moving image to obtain segmented images of each moving image;
detecting skeleton key points and postures of the portraits of the driving positions of the vehicles in the divided images, and determining portraits information of the drivers of the vehicles in the first image set according to detection results;
Determining the action track of the driver and the position of the berth where the vehicle is located in the first image set;
Determining a first distance change trend of the distance between the driver and the vehicle and a second distance change trend of the distance between the vehicle and the berth according to the action track of the driver and the position of the berth where the vehicle is located;
determining the in-out behavior of the vehicle according to the first distance change trend and the second distance change trend;
the determining the access behavior of the vehicle according to the first distance variation trend and the second distance variation trend includes:
If the first distance change trend is gradually reduced and the second distance change trend is gradually increased, determining that the vehicle leaves;
and if the first distance change trend is gradually increased and the second distance change trend is unchanged, determining that the vehicle enters the ground.
2. The method of claim 1, wherein the acquiring a first set of images including the vehicle in motion for a predetermined period of time comprises:
step A, continuously acquiring a second image set of a preset monitoring area in a preset time period, and judging whether the second image set contains images of a vehicle in a moving state or not;
if not, jumping to the step A until the second image set contains the images of the vehicle in a moving state;
and if so, determining the second image set as a first image set.
3. The method of claim 1, wherein determining a first distance trend of the distance between the driver and the vehicle and a second distance trend of the distance between the vehicle and the berth based on the trajectory of the driver and the position of the berth where the vehicle is located comprises:
determining a first change trend of the distance between the driver and the vehicle according to the action track of the driver;
And determining a second distance change trend of the distance between the vehicle and the berth in the first image set through a preset image recognition algorithm according to the determined position of the berth where the vehicle is located.
4. An apparatus for determining vehicle behavior based on pedestrian re-identification, comprising:
the acquisition and judgment module is used for acquiring an image of a preset monitoring area, identifying a current image and judging whether a vehicle exists in the current image;
The acquisition and determination module is used for acquiring a first image set containing the vehicle in a moving state in a preset time period, determining the portrait information in the first image set through a pedestrian re-identification algorithm, and determining the portrait information of a driver of the vehicle according to the portrait information in the first image set;
The acquisition and determination module comprises:
A second determining unit configured to extract a network model according to a predetermined feature, and determine, in the first image set, a feature of a portrait in each moving image in which the vehicle is in a moving state;
a calculating unit for calculating a feature distance in each moving image according to the feature;
The sorting unit is used for sorting the feature distances in the moving images and determining the portrait information in the vehicle from the moving images;
The acquisition and determination module comprises:
A third determining unit, configured to determine, according to the portrait information, portrait information in a driving state at the driving position of the vehicle;
A detection unit for detecting a position rectangular frame of a portrait in the vehicle driving position in each moving image through a predetermined feature extraction network model;
A dividing unit configured to divide each moving image for the portrait of the vehicle driving position according to the pixel probability of the portrait in each moving image based on the position rectangular frame;
a replacing unit, configured to perform background replacement on each of the segmented moving images to obtain segmented images of each of the moving images;
The detection and determination unit is used for detecting skeleton key points and postures of the portraits of the driving positions of the vehicles in the divided images and determining portraits information of the drivers of the vehicles in the first image set according to detection results;
The first determining module is used for determining the action track of the driver and the position of the berth where the vehicle is located in the first image set;
The second determining module is used for determining a first distance change trend of the distance between the driver and the vehicle and a second distance change trend of the distance between the vehicle and the berth according to the action track of the driver and the position of the berth where the vehicle is located;
The third determining module is used for determining the in-out behavior of the vehicle according to the first distance change trend and the second distance change trend;
The third determination module is specifically configured to
If the first distance change trend is gradually reduced and the second distance change trend is gradually increased, determining that the vehicle leaves;
and if the first distance change trend is gradually increased and the second distance change trend is unchanged, determining that the vehicle enters the ground.
5. The apparatus of claim 4, wherein the means for obtaining and determining comprises:
A judging unit, configured to continuously acquire a second image set of a predetermined monitoring area within a predetermined period of time, and judge whether the second image set contains an image of a vehicle in a moving state;
a jumping unit, configured to jump to the judging unit if the second image set does not include an image in which the vehicle is in a moving state;
and the first determining unit is used for determining the second image set as the first image set if the second image set is included.
6. The apparatus according to claim 4, wherein the second determining module is specifically configured to
Determining a first change trend of the distance between the driver and the vehicle according to the action track of the driver;
And determining a second distance change trend of the distance between the vehicle and the berth in the first image set through a preset image recognition algorithm according to the determined position of the berth where the vehicle is located.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110689288.8A CN113449624B (en) | 2021-06-22 | 2021-06-22 | Method and device for determining vehicle behavior based on pedestrian re-identification |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110689288.8A CN113449624B (en) | 2021-06-22 | 2021-06-22 | Method and device for determining vehicle behavior based on pedestrian re-identification |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113449624A CN113449624A (en) | 2021-09-28 |
| CN113449624B true CN113449624B (en) | 2024-05-14 |
Family
ID=77812125
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110689288.8A Active CN113449624B (en) | 2021-06-22 | 2021-06-22 | Method and device for determining vehicle behavior based on pedestrian re-identification |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113449624B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20060061680A (en) * | 2004-12-02 | 2006-06-08 | 주식회사 대우일렉트로닉스 | System and method for detecting location of car parking |
| CN106652052A (en) * | 2016-12-30 | 2017-05-10 | 苏州远扬智能科技有限公司 | Parking lot entrance and exit management system and parking lot entrance and exit management method |
| CN107680382A (en) * | 2017-02-24 | 2018-02-09 | 上海享泊信息科技有限公司 | Licence plate recognition method, module, cloud server and managing system of car parking |
| CN111353369A (en) * | 2019-10-16 | 2020-06-30 | 智慧互通科技有限公司 | Application method and system of high-order video of urban roadside parking in assisting criminal investigation |
| CN112820137A (en) * | 2020-12-31 | 2021-05-18 | 金茂智慧科技(广州)有限公司 | Parking lot management method and device |
| CN112836699A (en) * | 2020-11-30 | 2021-05-25 | 爱泊车美好科技有限公司 | Long-time multi-target tracking-based berth entrance and exit event analysis method |
-
2021
- 2021-06-22 CN CN202110689288.8A patent/CN113449624B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20060061680A (en) * | 2004-12-02 | 2006-06-08 | 주식회사 대우일렉트로닉스 | System and method for detecting location of car parking |
| CN106652052A (en) * | 2016-12-30 | 2017-05-10 | 苏州远扬智能科技有限公司 | Parking lot entrance and exit management system and parking lot entrance and exit management method |
| CN107680382A (en) * | 2017-02-24 | 2018-02-09 | 上海享泊信息科技有限公司 | Licence plate recognition method, module, cloud server and managing system of car parking |
| CN111353369A (en) * | 2019-10-16 | 2020-06-30 | 智慧互通科技有限公司 | Application method and system of high-order video of urban roadside parking in assisting criminal investigation |
| CN112836699A (en) * | 2020-11-30 | 2021-05-25 | 爱泊车美好科技有限公司 | Long-time multi-target tracking-based berth entrance and exit event analysis method |
| CN112820137A (en) * | 2020-12-31 | 2021-05-18 | 金茂智慧科技(广州)有限公司 | Parking lot management method and device |
Non-Patent Citations (2)
| Title |
|---|
| 基于多形态红外特征与深度学习的实时驾驶员疲劳检测;耿磊;梁晓昱;肖志涛;李月龙;;红外与激光工程(第02期);全文 * |
| 基于眼睛和嘴巴状态的驾驶员疲劳检测算法;邬敏杰;穆平安;张彩艳;;计算机应用与软件(第03期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113449624A (en) | 2021-09-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110163107B (en) | Method and device for recognizing roadside parking behavior based on video frames | |
| CN111339994B (en) | Method and device for judging temporary illegal parking | |
| CN111405196B (en) | Vehicle management method and system based on video splicing | |
| CN113055823B (en) | Method and device for managing shared bicycle based on road side parking | |
| CN110930756B (en) | Method and device for judging roadside parking based on image and geomagnetic sensor | |
| CN113205689B (en) | Multi-dimension-based roadside parking admission event judgment method and system | |
| CN111739338A (en) | Parking management method and system based on multiple types of sensors | |
| CN111611886B (en) | Parking information verification method and device based on vehicle behavior analysis | |
| CN113205691A (en) | Method and device for identifying vehicle position | |
| CN112861773A (en) | Multi-level-based berthing state detection method and system | |
| CN112836699A (en) | Long-time multi-target tracking-based berth entrance and exit event analysis method | |
| CN111340710A (en) | Method and system for acquiring vehicle information based on image stitching | |
| CN111931673A (en) | Vision difference-based vehicle detection information verification method and device | |
| CN113450575B (en) | Management method and device for roadside parking | |
| CN113205690B (en) | Roadside parking departure event judgment method and system based on multiple dimensions | |
| CN112766222B (en) | Method and device for assisting in identifying vehicle behavior based on berth line | |
| CN113869258A (en) | Traffic incident detection method and device, electronic equipment and readable storage medium | |
| CN111951601B (en) | Method and device for identifying parking positions of distribution vehicles | |
| CN113449605A (en) | Multi-dimension-based roadside vehicle illegal parking judgment method and system | |
| CN113449624B (en) | Method and device for determining vehicle behavior based on pedestrian re-identification | |
| CN113570871A (en) | Multidimensional vehicle personnel getting-on and getting-off judgment method and system | |
| CN113473077A (en) | Multi-dimension-based roadside reverse parking judgment method and system | |
| CN114694408B (en) | Parking order generation method and system based on multidimensional berth state | |
| CN117437792B (en) | Real-time road traffic state monitoring method, device and system based on edge calculation | |
| CN116824549B (en) | Target detection method and device based on multi-detection network fusion and vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Yan Jun Inventor after: Yang Xueming Inventor after: Wang Yiyao Inventor after: Liu Dong Inventor before: Yan Jun Inventor before: Yang Xueming |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |