WO2020061766A1 - Appareil et procédé de détection d'événement de véhicule, programme informatique et support lisible par ordinateur - Google Patents

Appareil et procédé de détection d'événement de véhicule, programme informatique et support lisible par ordinateur Download PDF

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Publication number
WO2020061766A1
WO2020061766A1 PCT/CN2018/107405 CN2018107405W WO2020061766A1 WO 2020061766 A1 WO2020061766 A1 WO 2020061766A1 CN 2018107405 W CN2018107405 W CN 2018107405W WO 2020061766 A1 WO2020061766 A1 WO 2020061766A1
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WO
WIPO (PCT)
Prior art keywords
vehicle
shape
pictures
change
time
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PCT/CN2018/107405
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English (en)
Chinese (zh)
Inventor
孙芃
Original Assignee
西门子股份公司
孙芃
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Publication date
Application filed by 西门子股份公司, 孙芃 filed Critical 西门子股份公司
Priority to PCT/CN2018/107405 priority Critical patent/WO2020061766A1/fr
Publication of WO2020061766A1 publication Critical patent/WO2020061766A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles

Definitions

  • the present invention relates to the technical field of traffic management, and in particular, to a device, a method, a computer program product, and a computer-readable medium for detecting a vehicle event.
  • embodiments of the present invention provide a device, a method, a computer program product, and a computer-readable medium for detecting a vehicle event, which are used to realize automatic detection of a vehicle event, which can save a lot of manpower, and have accurate and timely detection advantage.
  • a method for detecting a vehicle event including: acquiring a set of pictures sorted by time of a vehicle within a time period; and identifying, from the set of pictures, a vehicle's Changes in shape over time; determining whether the identified change is the same as a pattern of a vehicle shape that changes over time that is defined in advance; if the pattern is the same, it is determined that the vehicle has an event defined by the pattern.
  • the change is compared with a predefined pattern of the vehicle shape changing over time. Since the pattern of the vehicle shape changing over time corresponds to a vehicle event, it can be based on the vehicle shape over time Changes determine what kind of vehicle incident has occurred.
  • the change of the shape of the vehicle when the vehicle event occurs and the positional relationship between the vehicle and the passengers or goods related to the vehicle are defined in advance. Multiple vehicle shape changes over time. When the identified change in vehicle shape over time matches a certain pattern, it can be easily determined that a vehicle event defined by the pattern has occurred.
  • the embodiments of the present invention can be implemented on a vehicle event detection device deployed at a traffic scene, further ensuring event detection. Real-time performance, and through edge computing and processing, also reduces the processing load of the central node.
  • the changeable shape of the vehicle may be identified. At least one part; determining a change in shape of each part identified in each picture that is changeable over time within the time period.
  • the occupant of the vehicle may be identified for each of the group of pictures, and the positional relationship between the occupant of the vehicle and at least one part of the vehicle that can be changed is identified in each picture
  • identifying the cargo of the vehicle determining a positional relationship between the cargo of the vehicle identified in each of the pictures and at least one part of the vehicle that can be changed.
  • the identified change before judging whether the identified change is the same as a predefined pattern of a vehicle shape changing with time, it further includes: identifying the type of the vehicle from the set of pictures; judging the identified all Whether the change is the same as a pre-defined mode of vehicle shape changing with time, and includes: comparing the identified change with at least one mode of vehicle shape changing over time corresponding to the identified type of the vehicle .
  • the calculation amount of the pattern matching can be further reduced, and the accuracy of the pattern matching can be further improved by first identifying the type of the vehicle.
  • a vehicle event detection device including:
  • a picture acquisition module for obtaining a set of pictures sorted by time of a vehicle within a time period
  • a shape change detection module configured to identify, from the set of pictures, a change in shape of the vehicle over time in the time period
  • a pattern matching module configured to determine whether the identified change is the same as a predefined pattern of a vehicle shape changing with time
  • An event judging module if the change is the same as a predefined pattern of a vehicle shape changing with time, determining that the vehicle has an event defined by the pattern.
  • the change is compared with a predefined pattern of the shape of the vehicle over time. Since the pattern of the shape of the vehicle over time corresponds to a vehicle event, it can be based on the shape of the vehicle over time. Changes determine what kind of vehicle incident has occurred.
  • the change of the shape of the vehicle when the vehicle event occurs and the positional relationship between the vehicle and the passengers or goods related to the vehicle are defined in advance. Multiple vehicle shape changes over time. When the identified change in vehicle shape over time matches a certain pattern, it can be easily determined that a vehicle event defined by the pattern has occurred.
  • the embodiments of the present invention can be implemented on a vehicle event detection device deployed at a traffic scene, further ensuring event detection. Real-time performance, and through edge computing and processing, also reduces the processing load of the central node.
  • the shape change detection module is specifically configured to: for each of the group of pictures, identify at least a part where the shape of the vehicle is changeable; determine that the shape recognized in each picture is changeable The shape of each of the parts changes over time during the time period.
  • the shape change detection module may identify the occupant of the vehicle for each of the set of pictures; and determine that the occupants of the vehicle and the changeable of the vehicle are identified in each of the pictures. Position relationship of at least one part of; and for each of the set of pictures, identifying the goods of the vehicle; determining at least one of the pictures identifying the goods of the vehicle and the changeable of the vehicle in each picture Partial positional relationship.
  • the device further includes: a vehicle type recognition module, configured to identify from the set of pictures before determining whether the identified change is the same as a predefined pattern of a vehicle shape changing with time.
  • the type of the vehicle the pattern matching module is specifically configured to compare the identified change with at least one pattern of vehicle shape changes over time corresponding to the identified type of the vehicle.
  • the calculation amount of the pattern matching can be further reduced, and the accuracy of the pattern matching can be further improved by first identifying the type of the vehicle.
  • a vehicle event detection device including: at least one memory for storing computer-readable code; and at least one processor for calling the computer-readable code to execute the first aspect or the first aspect Method provided by any of the possible implementations.
  • the ERP system device provided by the second aspect, any possible implementation manner of the second aspect, the third aspect, or any possible implementation manner of the third aspect may be deployed at a traffic site to perform edge computing.
  • a computer program product is provided that is tangibly stored on a computer-readable medium and includes computer-executable instructions that, when executed, cause at least one processor to execute a first The method provided by one aspect or any possible implementation manner of the first aspect.
  • a computer-readable medium stores computer-readable instructions, and the computer-readable instructions, when executed by a processor, cause the processor to execute the first aspect, or The method provided in any possible implementation of the first aspect.
  • the judged event includes one of the following types: vehicle side door opened; vehicle side door closed; vehicle tail door opened; vehicle tail door closed; vehicle occupant Get on; get off the vehicle; load; unload.
  • FIG. 1 is a schematic structural diagram of a traffic management system according to a first embodiment of the present invention.
  • FIG. 2a is a flowchart of a method for detecting a vehicle event according to a second embodiment of the present invention.
  • FIG. 2b is a flowchart of a method for detecting a vehicle event according to a third embodiment of the present invention.
  • FIG. 3 is a schematic structural diagram of a vehicle event detection device according to a fourth embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of a vehicle event detection device according to a fifth embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of a vehicle event detection device according to a sixth embodiment of the present invention.
  • 6 to 8 are schematic diagrams of detecting a shape change of a vehicle in each embodiment of the present invention.
  • FIG. 9 and FIG. 11 are schematic diagrams of detecting a vehicle occupant getting off event according to an embodiment of the present invention.
  • FIG. 10 is a schematic diagram of detecting a boarding event of a vehicle occupant according to an embodiment of the present invention.
  • FIG. 12 is a schematic diagram of detecting an unloading event in an embodiment of the present invention.
  • FIG. 13 is a schematic diagram of detecting a loading event in an embodiment of the present invention.
  • Vehicle event detection device 40 Client Zh S101: Obtain a picture sequence S102: Recognize changes in vehicle shape S103: Vehicle shape change pattern matching S104: Determine the occurrence of a vehicle event Zh Zh S201: Obtaining a picture sequence S202: Identify vehicle shape changes S202 ’: Identify the type of vehicle S203: Vehicle shape change pattern matching S204: Determine the occurrence of a vehicle event Zh 301: Picture acquisition module 302: Shape change detection module 303: Pattern matching module 304: Event judgment module 302 ’: Vehicle type identification module Zh 305: Memory 306: Processor 307: Communication module
  • the change is compared with a predefined pattern of the shape of the vehicle with time.
  • the mode corresponds to vehicle events, so you can determine what kind of vehicle event has occurred based on changes in vehicle shape over time.
  • the change of the shape of the vehicle when the vehicle event occurs and the positional relationship between the vehicle and the passengers or goods related to the vehicle are defined in advance. Multiple vehicle shape changes over time. When the identified change in vehicle shape over time matches a certain pattern, it can be easily determined that a vehicle event defined by the pattern has occurred.
  • the embodiments of the present invention can be implemented on a vehicle event detection device deployed at a traffic scene, further ensuring event detection. Real-time performance, and through edge computing and processing, also reduces the processing load of the central node.
  • FIG. 1 shows a traffic management system 100 according to an embodiment of the present invention.
  • the camera 10 is used to capture pictures or videos
  • the vehicle event detection device 30 is used to perform image processing on the pictures or videos captured by the camera 10, and determine whether a vehicle event has occurred and what kind of vehicle has occurred according to the results of the image processing. event.
  • the judgment results and related pictures or videos can be stored in the vehicle event detection device 20 or the cloud 20 side, and the client 40 (computer or mobile terminal) can log in to the cloud 20 side to obtain the detection results of the vehicle event, and can obtain related Pictures or videos, and further, various applications and the like running on the client 40 can be implemented according to the detection result of the vehicle event.
  • the vehicle event detection device 30 may be deployed at a traffic scene, and may be integrated with the camera 10 on the same hardware device in hardware implementation, or may be implemented by a separate hardware device.
  • the vehicle event detection process implemented by the vehicle event detection device 30 may refer to the second embodiment and the third embodiment described below.
  • the method for detecting a vehicle event provided in the second embodiment may include the following steps:
  • the vehicle event detection device 30 can capture the video or picture at the traffic scene through the camera 10. It should be noted that if the video is captured, multiple pictures ordered by time can be intercepted from the video; if If you are taking pictures, you need to mark the time of taking each picture to ensure that you get a set of pictures ordered by time.
  • At least one part of the vehicle shape that can be changed such as a side door, a tail door, a occupant of the vehicle, a cargo of the vehicle, and the like can be identified;
  • the shape of each part can change over time during a period of time.
  • the camera 10 captures the upper left and upper right pictures in FIG. 8.
  • a bounding box can be used to frame the target car in the two pictures.
  • the shape of the tail gate of the vehicle that can be changed is also bounded by the bounding box, then the change in the shape of the tail gate of the vehicle can be determined.
  • the positional relationship between the occupant of the vehicle and at least one part of the vehicle that can be changed in each of the pictures can be further determined. If the cargo of the vehicle is identified from the set of pictures, the positional relationship between the identified cargo of the vehicle and at least one part of the vehicle that can be changed can be further determined.
  • the camera 10 has taken three pictures from left to right, and can recognize the changeable shape of the side door of the vehicle and the occupant of the vehicle through target recognition, and can identify the vehicle Relationship between the occupants and the side doors of the vehicle.
  • the camera has taken three pictures from left to right, and can recognize the changeable shapes of the rear door of the vehicle, the occupant of the vehicle, and the cargo of the vehicle through target recognition, and can identify The positional relationship between the occupant of the vehicle and the tailgate of the vehicle, the positional relationship between the cargo of the vehicle and the tailgate of the vehicle, and the positional relationship between the occupant of the vehicle and the acquisition of the vehicle of the vehicle.
  • the pattern matching can be performed through the following step S103 and then the vehicle event that has occurred can be determined through step S104.
  • step S102 it is determined whether the change identified in step S102 is the same as a pattern of a shape of a vehicle that changes over time in advance.
  • step S104 If the change identified in step S102 is the same as a predefined pattern of a vehicle shape changing with time, it is determined that the vehicle has an event defined by the pattern.
  • the pattern of the vehicle shape changing with time, and the correspondence between the events defined by the pattern can be shown in the following table.
  • a machine vision algorithm can be used to trigger an event by identifying changes in the shape of the vehicle over time. Since the machine vision algorithm does not require high processing capabilities, the above process can be deployed by edge devices deployed at the traffic site That is, the vehicle event detection device 30 is implemented. In addition, an artificial intelligence algorithm may be used to perform the above steps S103 and S104 for pattern matching and event determination.
  • the artificial intelligence algorithm can identify the following steps:
  • the side door is opened (the profile of the side of the vehicle changes).
  • the vehicle occupant getting off the boundary of the vehicle occupant
  • the frame is adjacent to or partially overlaps the bounding box of the vehicle, that is, the positional relationship between the occupant of the vehicle and the part where the shape of the vehicle can be changed).
  • finite state machine can identify the following steps:
  • the tailgate is opened (the outline of the side of the vehicle changes), and a cargo is detected in the vehicle (the bounding box of the cargo overlaps with the bounding box of the vehicle).
  • the tailgate opens, and passengers and cargo are detected outside the vehicle.
  • steps S201, S202, S203, and S204 included in the method for detecting a vehicle event provided in the third embodiment may refer to the foregoing steps S101, S102, S103, and S104, respectively.
  • the type of the vehicle can be further identified.
  • the identified change may be compared with at least one pattern of vehicle shape changes over time corresponding to the identified type of vehicle. This can reduce the number of pattern matching and reduce the amount of calculation.
  • the target type may be further identified, such as: a vehicle, a side door of the vehicle, a tail door of the vehicle, a cargo, a passenger of the vehicle, and the like.
  • a vehicle a side door of the vehicle
  • a tail door of the vehicle a cargo
  • a passenger of the vehicle a passenger of the vehicle
  • the vehicle event detection device 30 provided in the fourth embodiment may include:
  • a picture acquisition module 301 for obtaining a set of pictures sorted by time of a vehicle within a time period
  • a shape change detection module 302 for identifying the change of the shape of the vehicle over time from a set of pictures
  • a pattern matching module 303 configured to determine whether the identified change is the same as a predefined pattern of a vehicle shape changing with time
  • An event judging module 304 determines that the vehicle has an event defined by the mode if the change is the same as a predefined pattern of a vehicle shape changing with time.
  • the shape change detection module 302 is specifically configured to:
  • the shape change detection module 302 may be specifically configured to: for each of a group of pictures, identify the occupant of the vehicle; determine the identities of the vehicle and the changeable at least one part of the vehicle in each of the pictures. Positional relationship; and for each of a set of pictures, identifying the cargo of the vehicle; determining the positional relationship of the cargo of the identified vehicle in each of the pictures and at least one part of the vehicle that can be changed.
  • the event determined by the event determination module 304 includes one of the following types: vehicle side door open, vehicle side door closed, vehicle tail door opened, vehicle tail door closed, vehicle occupant getting on, vehicle occupant getting off, loading Cargo, unloading.
  • the picture acquisition module 301 may be used to complete the foregoing step S101
  • the shape change detection module 302 may be used to complete the foregoing step S102
  • the pattern matching module 303 may be used to complete the foregoing
  • the event determination module 304 may be used to complete the foregoing step S104.
  • the vehicle event detection device 30 provided in the fifth embodiment may further include a vehicle type recognition module 302 'for determining whether the recognized change is different from a predefined shape of the vehicle over time. Before the patterns are the same, the type of the vehicle is identified from a set of pictures; and the pattern matching module 303 is specifically configured to compare the identified change with at least one pattern of the shape of the vehicle that changes over time corresponding to the identified type of vehicle. .
  • FIG. 5 is a schematic structural diagram of a vehicle event detection device 30 according to an eighth embodiment. As shown in FIG. 5, the device may include:
  • At least one memory 305 configured to store computer-readable code
  • At least one processor 306 is configured to call a computer-readable code to execute a method for detecting a vehicle event provided by the foregoing embodiments.
  • the at least one memory 305 and the at least one processor 306 may be connected through a bus.
  • the device may further provide at least one communication interface 307 for communicating with the camera 10 and the cloud 20, and transmitting pictures and picture information.
  • the communication interface 307 and the memory 305 and the processor 306 can also communicate through a bus.
  • the modules and the like included in the vehicle event detection device 30 in Embodiments 5 to 6 can be regarded as program modules in computer-readable codes stored in at least one memory 305 shown in FIG. 5.
  • At least one processor 306 calls to execute a method for detecting a vehicle event provided by an embodiment of the present invention.
  • the program module may be included in the computing device in the form of an operating system, an application program module, and other program modules, and may also be physically stored in several known memory devices.
  • the program module may include, but is not limited to, a routine, a subroutine, a program, an object, a component, and a data structure for performing a specific operation or a type of specific abstract data to be described in accordance with the present invention.
  • the above-mentioned modules can also be regarded as various functional modules implemented by a combination of hardware and software, and various functions involved in the vehicle event detection device 30 when executing the vehicle event detection method.
  • the above-mentioned modules can also be regarded as various functional modules implemented by hardware, which are used to implement various functions involved in the vehicle event detection device 30 when performing the vehicle event detection method, such as the methods involved in the vehicle event detection method in advance.
  • the control logic of each process is burned into, for example, a Field-Programmable Gate Array (FPGA) chip or a Complex Programmable Logic Device (CPLD), and these chips or devices execute the above modules
  • FPGA Field-Programmable Gate Array
  • CPLD Complex Programmable Logic Device
  • an embodiment of the present invention further provides a computer-readable medium.
  • the computer-readable medium stores computer-readable instructions. When the computer-readable instructions are executed by a processor, the method for detecting a processor vehicle event.
  • Examples of computer-readable media include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD + RW), magnetic tape, non- Volatile memory card and ROM.
  • the computer-readable instructions may be downloaded from a server computer or the cloud by a communication network.
  • embodiments of the present invention provide a method, a device, a computer-readable medium, a computer program product, and a traffic management system for detecting a vehicle event.
  • the change is compared with a predefined pattern of the shape of the vehicle over time. Since the pattern of the shape of the vehicle over time corresponds to a vehicle event, it can be based on the shape of the vehicle over time. Changes automatically determine what kind of vehicle event has occurred.
  • the execution order of each step is not fixed and can be adjusted as needed.
  • the system structure described in the above embodiments may be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or may be implemented by multiple Some components in separate devices are implemented together.

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Abstract

L'invention concerne un appareil et un procédé de détection d'événement de véhicule, ainsi qu'un programme informatique et un support lisible par ordinateur. Le procédé de détection comprend les étapes suivantes consistant à : acquérir un groupe d'images, agencées dans un ordre chronologique, d'un véhicule durant une période (S101) ; identifier, à partir du groupe d'images, un changement au fil du temps de la forme du véhicule durant la période (S102) ; déterminer si le changement identifié est identique à un mode prédéfini d'un changement au fil du temps d'une forme de véhicule (S103) ; et si les deux sont identiques, déterminer qu'un événement défini par mode s'est produit dans le véhicule (S104).
PCT/CN2018/107405 2018-09-25 2018-09-25 Appareil et procédé de détection d'événement de véhicule, programme informatique et support lisible par ordinateur WO2020061766A1 (fr)

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PCT/CN2018/107405 WO2020061766A1 (fr) 2018-09-25 2018-09-25 Appareil et procédé de détection d'événement de véhicule, programme informatique et support lisible par ordinateur

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PCT/CN2018/107405 WO2020061766A1 (fr) 2018-09-25 2018-09-25 Appareil et procédé de détection d'événement de véhicule, programme informatique et support lisible par ordinateur

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Citations (6)

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Publication number Priority date Publication date Assignee Title
CN102637257A (zh) * 2012-03-22 2012-08-15 北京尚易德科技有限公司 一种基于视频的交通车辆检测识别系统和方法
CN103258432A (zh) * 2013-04-19 2013-08-21 西安交通大学 基于视频的交通事故自动识别处理方法和系统
KR20140117116A (ko) * 2013-03-26 2014-10-07 한국도로공사 영상처리 기술을 이용한 차량의 축조작 검출 방법
CN106600977A (zh) * 2017-02-13 2017-04-26 深圳英飞拓科技股份有限公司 基于多特征识别的违停检测方法及系统
CN206741700U (zh) * 2017-05-18 2017-12-12 南京云计趟信息技术有限公司 一种用于渣土车货箱监测的视频识别系统
CN107545614A (zh) * 2016-06-27 2018-01-05 福特全球技术公司 具有事件记录的车辆

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102637257A (zh) * 2012-03-22 2012-08-15 北京尚易德科技有限公司 一种基于视频的交通车辆检测识别系统和方法
KR20140117116A (ko) * 2013-03-26 2014-10-07 한국도로공사 영상처리 기술을 이용한 차량의 축조작 검출 방법
CN103258432A (zh) * 2013-04-19 2013-08-21 西安交通大学 基于视频的交通事故自动识别处理方法和系统
CN107545614A (zh) * 2016-06-27 2018-01-05 福特全球技术公司 具有事件记录的车辆
CN106600977A (zh) * 2017-02-13 2017-04-26 深圳英飞拓科技股份有限公司 基于多特征识别的违停检测方法及系统
CN206741700U (zh) * 2017-05-18 2017-12-12 南京云计趟信息技术有限公司 一种用于渣土车货箱监测的视频识别系统

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