CN116127165A - Vehicle position updating method and device, storage medium and electronic device - Google Patents

Vehicle position updating method and device, storage medium and electronic device Download PDF

Info

Publication number
CN116127165A
CN116127165A CN202211733173.5A CN202211733173A CN116127165A CN 116127165 A CN116127165 A CN 116127165A CN 202211733173 A CN202211733173 A CN 202211733173A CN 116127165 A CN116127165 A CN 116127165A
Authority
CN
China
Prior art keywords
vehicle
target
moment
position information
loss value
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.)
Pending
Application number
CN202211733173.5A
Other languages
Chinese (zh)
Inventor
侯壮
李冬冬
李乾坤
陈向阳
吴函
高存璋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang Dahua Technology Co Ltd
Original Assignee
Zhejiang Dahua Technology Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Zhejiang Dahua Technology Co Ltd filed Critical Zhejiang Dahua Technology Co Ltd
Priority to CN202211733173.5A priority Critical patent/CN116127165A/en
Publication of CN116127165A publication Critical patent/CN116127165A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/907Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/909Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/50Context or environment of the image
    • G06V20/56Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Library & Information Science (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Navigation (AREA)

Abstract

本发明实施例提供了一种车辆的位置更新方法、装置、存储介质及电子装置,其中,该方法包括:根据目标车辆在第一时刻的位置信息,确定目标车辆在第二时刻的预测位置信息,其中,第二时刻晚于第一时刻;获取在第二时刻位于目标范围内的车辆,得到第一车辆集合,其中,目标范围是根据目标车辆在第一时刻的位置信息确定的;根据预测位置信息在第一车辆集合中确定与目标车辆匹配的第一车辆,并将目标车辆在第二时刻的位置信息确定为第一车辆的位置信息。通过本发明实施例,解决了相关技术中存在的目标车辆的位置更新不准确的问题。

Figure 202211733173

An embodiment of the present invention provides a vehicle location update method, device, storage medium, and electronic device, wherein the method includes: determining the predicted location information of the target vehicle at the second moment according to the location information of the target vehicle at the first moment , where the second moment is later than the first moment; obtain the vehicles located within the target range at the second moment, and obtain the first vehicle set, wherein the target range is determined according to the position information of the target vehicle at the first moment; according to the prediction The location information determines the first vehicle that matches the target vehicle in the first vehicle set, and determines the location information of the target vehicle at the second moment as the location information of the first vehicle. Through the embodiments of the present invention, the problem of inaccurate update of the position of the target vehicle existing in the related art is solved.

Figure 202211733173

Description

车辆的位置更新方法、装置、存储介质及电子装置Vehicle location updating method, device, storage medium and electronic device

技术领域Technical Field

本发明实施例涉及车辆检测领域,具体而言,涉及一种车辆的位置更新方法、装置、存储介质及电子装置。The embodiments of the present invention relate to the field of vehicle detection, and in particular, to a method, device, storage medium and electronic device for updating the position of a vehicle.

背景技术Background Art

随着深度学习网络模型的不断优化升级,以及针于车辆问题的适应性研究,基于视频图像的车辆检测和跟踪问题得到了极大的解决,车辆目标的实时检测和跟踪能力得到了极大的提升。但并非任何场景下都可以实现高精度的目标检测和跟踪能力,例如上下班的车辆高峰期、红灯时的停车排队等情况,车道内车辆过于密集,时常存在车辆目标的多检、漏检、目标ID跳变的问题,而车辆目标的多检、漏检、目标ID跳变会导致目标车辆的位置更新不准确,进而制约着车辆计数的准确性。而在因此相关技术中存在目标车辆的位置更新不准确的问题。With the continuous optimization and upgrading of deep learning network models, as well as adaptive research on vehicle issues, the vehicle detection and tracking problems based on video images have been greatly solved, and the real-time detection and tracking capabilities of vehicle targets have been greatly improved. However, high-precision target detection and tracking capabilities cannot be achieved in all scenarios. For example, during rush hour, parking queues at red lights, and other situations, the vehicles in the lanes are too dense, and there are often problems such as multiple inspections, missed inspections, and target ID jumps of vehicle targets. Multiple inspections, missed inspections, and target ID jumps of vehicle targets will lead to inaccurate updates of the target vehicle's position, which in turn restricts the accuracy of vehicle counting. Therefore, in the relevant technologies, there is a problem of inaccurate updates of the target vehicle's position.

针对相关技术中存在的目标车辆的位置更新不准确的问题,目前尚未提出有效的解决方案。With regard to the problem of inaccurate position update of the target vehicle existing in the related art, no effective solution has been proposed yet.

发明内容Summary of the invention

本发明实施例提供了一种车辆的位置更新方法、装置、存储介质及电子装置,以至少解决相关技术中存在的目标车辆的位置更新不准确的问题。Embodiments of the present invention provide a vehicle position update method, device, storage medium and electronic device to at least solve the problem of inaccurate position update of a target vehicle existing in the related art.

根据本发明的一个实施例,提供了一种车辆的位置更新方法,包括:根据目标车辆在第一时刻的位置信息,确定所述目标车辆在第二时刻的预测位置信息,其中,所述第二时刻晚于所述第一时刻;获取在所述第二时刻位于目标范围内的车辆,得到第一车辆集合,其中,所述目标范围是根据所述目标车辆在所述第一时刻的位置信息确定的;根据所述预测位置信息在所述第一车辆集合中确定与所述目标车辆匹配的第一车辆,并将所述第一车辆的位置信息确定为所述目标车辆在所述第二时刻的位置信息。According to one embodiment of the present invention, a vehicle position update method is provided, comprising: determining predicted position information of the target vehicle at a second moment based on position information of the target vehicle at a first moment, wherein the second moment is later than the first moment; acquiring vehicles within a target range at the second moment to obtain a first vehicle set, wherein the target range is determined based on the position information of the target vehicle at the first moment; determining a first vehicle matching the target vehicle in the first vehicle set based on the predicted position information, and determining the position information of the first vehicle as the position information of the target vehicle at the second moment.

根据本发明的又一个实施例,还提供了一种车辆的位置更新装置,包括:第一确定模块,用于根据目标车辆在第一时刻的位置信息,确定所述目标车辆在第二时刻的预测位置信息,其中,所述第二时刻晚于所述第一时刻;获取模块,用于获取在所述第二时刻位于目标范围内的车辆,得到第一车辆集合,其中,所述目标范围是根据所述目标车辆在所述第一时刻的位置信息确定的;第二确定模块,用于根据所述预测位置信息在所述第一车辆集合中确定与所述目标车辆匹配的第一车辆,并将所述第一车辆的位置信息确定为所述目标车辆在所述第二时刻的位置信息。According to another embodiment of the present invention, a vehicle position update device is also provided, including: a first determination module, used to determine the predicted position information of the target vehicle at a second moment based on the position information of the target vehicle at a first moment, wherein the second moment is later than the first moment; an acquisition module, used to acquire vehicles that are located within a target range at the second moment, and obtain a first vehicle set, wherein the target range is determined based on the position information of the target vehicle at the first moment; a second determination module, used to determine a first vehicle matching the target vehicle in the first vehicle set based on the predicted position information, and determine the position information of the first vehicle as the position information of the target vehicle at the second moment.

根据本发明的又一个实施例,还提供了一种计算机可读存储介质,所述计算机可读存储介质中存储有计算机程序,其中,所述计算机程序被设置为运行时执行上述任一项方法实施例中的步骤。According to yet another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any one of the above method embodiments when running.

根据本发明的又一个实施例,还提供了一种电子装置,包括存储器和处理器,所述存储器中存储有计算机程序,所述处理器被设置为运行所述计算机程序以执行上述任一项方法实施例中的步骤。According to yet another embodiment of the present invention, there is provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

通过本发明,通过预测目标车辆在第二时刻的位置信息,根据预测的位置信息在第一车辆集合中确定与目标车辆匹配的第一车辆,准确的在第二时刻识别出的车辆中找到与目标车辆匹配的车辆,并将目标车辆在第二时刻的位置信息确定为第一车辆的位置信息,实现了目标车辆的位置在不同时刻的准确更新,因此,解决了相关技术中存在的目标车辆的位置更新不准确的问题,达到了提高目标车辆的位置更新的准确性的效果。Through the present invention, by predicting the position information of the target vehicle at the second moment, determining the first vehicle matching the target vehicle in the first vehicle set according to the predicted position information, accurately finding the vehicle matching the target vehicle among the vehicles identified at the second moment, and determining the position information of the target vehicle at the second moment as the position information of the first vehicle, the accurate update of the position of the target vehicle at different moments is achieved. Therefore, the problem of inaccurate position update of the target vehicle existing in the related art is solved, and the effect of improving the accuracy of the position update of the target vehicle is achieved.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

图1是本发明实施例的车辆的位置更新方法的移动终端硬件结构框图;1 is a block diagram of the hardware structure of a mobile terminal of a vehicle location update method according to an embodiment of the present invention;

图2是根据本发明实施例的车辆的位置更新方法的流程图;FIG2 is a flow chart of a method for updating a vehicle's position according to an embodiment of the present invention;

图3是根据本发明实施例的目标车辆检测区域的示意图;FIG3 is a schematic diagram of a target vehicle detection area according to an embodiment of the present invention;

图4是根据本发明实施例的确定第一车辆集合的示意图;FIG4 is a schematic diagram of determining a first vehicle set according to an embodiment of the present invention;

图5是根据本发明具体实施例的车辆的位置更新的流程示意图;5 is a schematic diagram of a process of updating the position of a vehicle according to a specific embodiment of the present invention;

图6是根据本发明实施例的车辆的位置更新装置的结构框图。FIG. 6 is a structural block diagram of a device for updating a position of a vehicle according to an embodiment of the present invention.

具体实施方式DETAILED DESCRIPTION

下文中将参考附图并结合实施例来详细说明本发明的实施例。Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

需要说明的是,本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象,而不必用于描述特定的顺序或先后次序。It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

本申请实施例中所提供的方法实施例可以在移动终端、计算机终端或者类似的运算装置中执行。以运行在移动终端上为例,图1是本发明实施例的车辆的位置更新方法的移动终端硬件结构框图。如图1所示,移动终端可以包括一个或多个(图1中仅示出一个)处理器102(处理器102可以包括但不限于微处理器MCU或可编程逻辑器件FPGA等的处理装置)和用于存储数据的存储器104,其中,上述移动终端还可以包括用于通信功能的传输设备106以及输入输出设备108。本领域普通技术人员可以理解,图1所示的结构仅为示意,其并不对上述移动终端的结构造成限定。例如,移动终端还可包括比图1中所示更多或者更少的组件,或者具有与图1所示不同的配置。The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, FIG1 is a block diagram of the hardware structure of a mobile terminal of the vehicle location update method of an embodiment of the present invention. As shown in FIG1 , the mobile terminal may include one or more (only one is shown in FIG1 ) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that the structure shown in FIG1 is only for illustration and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than those shown in FIG1 , or have a configuration different from that shown in FIG1 .

存储器104可用于存储计算机程序,例如,应用软件的软件程序以及模块,如本发明实施例中的车辆的位置更新方法对应的计算机程序,处理器102通过运行存储在存储器104内的计算机程序,从而执行各种功能应用以及数据处理,即实现上述的方法。存储器104可包括高速随机存储器,还可包括非易失性存储器,如一个或者多个磁性存储装置、闪存、或者其他非易失性固态存储器。在一些实例中,存储器104可进一步包括相对于处理器102远程设置的存储器,这些远程存储器可以通过网络连接至移动终端。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the vehicle location update method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

传输装置106用于经由一个网络接收或者发送数据。上述的网络具体实例可包括移动终端的通信供应商提供的无线网络。在一个实例中,传输装置106包括一个网络适配器(Network Interface Control ler,简称为NIC),其可通过基站与其他网络设备相连从而可与互联网进行通讯。在一个实例中,传输装置106可以为射频(Radio Frequency,简称为RF)模块,其用于通过无线方式与互联网进行通讯。The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

在本实施例中提供了一种车辆的位置更新方法,图2是根据本发明实施例的车辆的位置更新方法的流程图,如图2所示,该流程包括如下步骤:In this embodiment, a method for updating the position of a vehicle is provided. FIG. 2 is a flow chart of the method for updating the position of a vehicle according to an embodiment of the present invention. As shown in FIG. 2 , the process includes the following steps:

步骤S202,根据目标车辆在第一时刻的位置信息,确定所述目标车辆在第二时刻的预测位置信息,其中,所述第二时刻晚于所述第一时刻;Step S202, determining predicted position information of the target vehicle at a second moment according to the position information of the target vehicle at a first moment, wherein the second moment is later than the first moment;

步骤S204,获取在所述第二时刻位于目标范围内的车辆,得到第一车辆集合,其中,所述目标范围是根据所述目标车辆在所述第一时刻的位置信息确定的;Step S204, acquiring vehicles located within a target range at the second moment to obtain a first vehicle set, wherein the target range is determined according to the position information of the target vehicles at the first moment;

步骤S206,根据所述预测位置信息在所述第一车辆集合中确定与所述目标车辆匹配的第一车辆,并将所述第一车辆的位置信息确定为所述目标车辆在所述第二时刻的位置信息。Step S206: determining a first vehicle matching the target vehicle in the first vehicle set according to the predicted position information, and determining the position information of the first vehicle as the position information of the target vehicle at the second moment.

在本实施例中,目标车辆是在第一时刻拍摄的第一图像中识别出的车辆,第一图像中的目标车辆的位置映射到实际空间中,得到目标车辆在第一时刻的位置信息,通过目标车辆在第一时刻的位置信息预测出目标车辆在第二时刻的位置信息,得到目标车辆在第二时刻的预测位置信息。In this embodiment, the target vehicle is a vehicle identified in a first image taken at a first moment. The position of the target vehicle in the first image is mapped to the actual space to obtain the position information of the target vehicle at the first moment. The position information of the target vehicle at the first moment is predicted to obtain the predicted position information of the target vehicle at the second moment.

在第二时刻拍摄的第二图片中识别出多个车辆,并确定了各个车辆在第二时刻时在实际空间的位置,进行目标跟踪,在第二图片中识别出的多个车辆中查找到第一车辆是目标车辆,并将第一车辆在第二时刻的位置信息确定为目标车辆在第二时刻的位置信息。Multiple vehicles are identified in the second picture taken at the second moment, and the positions of each vehicle in the actual space at the second moment are determined, and target tracking is performed. The first vehicle is found to be the target vehicle among the multiple vehicles identified in the second picture, and the position information of the first vehicle at the second moment is determined as the position information of the target vehicle at the second moment.

通过与第二图片中识别出的多个车辆分别进行匹配操作,确定哪个车辆是目标车辆,在开始进行匹配操作之前先将第二图片中识别出的多个车辆进行初步筛选:已知目标车辆在第一时刻的位置,则第二时刻时目标车辆的位置不会距离目标车辆在第一时刻的位置太远,因此,通过目标车辆在第一时刻的位置确定一个目标范围,将在第二时刻位于目标范围内的车辆添加至第一车辆集合中,根据预测位置信息在第一车辆中查找目标车辆匹配的第一车辆,将第一车辆确定为目标车辆,且将第一车辆的位置信息确定为目标车辆在第二时刻的位置信息。By performing matching operations with multiple vehicles identified in the second image respectively, determine which vehicle is the target vehicle. Before starting the matching operation, perform a preliminary screening on the multiple vehicles identified in the second image: if the position of the target vehicle at the first moment is known, then the position of the target vehicle at the second moment will not be too far away from the position of the target vehicle at the first moment. Therefore, a target range is determined by the position of the target vehicle at the first moment, and the vehicles within the target range at the second moment are added to the first vehicle set. The first vehicle matching the target vehicle is searched in the first vehicle according to the predicted position information, the first vehicle is determined as the target vehicle, and the position information of the first vehicle is determined as the position information of the target vehicle at the second moment.

通过上述步骤,通过预测目标车辆在第二时刻的位置信息,根据预测的位置信息在第一车辆集合中确定与目标车辆匹配的第一车辆,准确的在第二时刻识别出的车辆中找到与目标车辆匹配的车辆,并将第一车辆的位置信息确定为目标车辆在第二时刻的位置信息,实现了目标车辆的位置在不同时刻的准确更新,因此,解决了相关技术中存在的目标车辆的位置更新不准确的问题,达到了提高目标车辆的位置更新的准确性的效果。Through the above steps, by predicting the position information of the target vehicle at the second moment, determining the first vehicle that matches the target vehicle in the first vehicle set according to the predicted position information, accurately finding the vehicle that matches the target vehicle among the vehicles identified at the second moment, and determining the position information of the first vehicle as the position information of the target vehicle at the second moment, the accurate update of the position of the target vehicle at different moments is achieved. Therefore, the problem of inaccurate position update of the target vehicle existing in the related art is solved, and the effect of improving the accuracy of the position update of the target vehicle is achieved.

可选的,可以设置专门的目标车辆检测区域,图3是根据本发明实施例的目标车辆检测区域的示意图,如图3所示,目标车辆检测区域可以设置在距离路口预设距离的地方,该区域内相邻车辆间隔明显,车辆特征更加准确,视频车辆目标检测的准确性高,产生目标漏检、误检的概率较低,为车辆统计环节提供准确的车辆个数和位置信息。Optionally, a special target vehicle detection area can be set up. Figure 3 is a schematic diagram of a target vehicle detection area according to an embodiment of the present invention. As shown in Figure 3, the target vehicle detection area can be set at a preset distance from the intersection. Adjacent vehicles in this area are clearly spaced apart, vehicle features are more accurate, video vehicle target detection is highly accurate, and the probability of missed or false detection of targets is low, thereby providing accurate vehicle count and location information for the vehicle statistics link.

可以在目标检测区域进行车辆检测,获取当前在目标检测区域内的车辆的个数和位置系您,建立当前时刻目标序列以及对应的目标框序列:You can perform vehicle detection in the target detection area, obtain the number and position of vehicles currently in the target detection area, and establish the current target sequence and the corresponding target frame sequence:

Vtrg1,Boxtrg1 V trg1 , Box trg1

Vtrg2,Boxtrg2 V trg2 , Box trg2

Vtrg3,Boxtrg3 V trg3 , Box trg3

其中,Vtrgi表示车辆ID,Boxtrgi表示车辆对应的目标框位置。Among them, V trgi represents the vehicle ID, and Box trgi represents the target box position corresponding to the vehicle.

Figure BDA0004032244440000051
Figure BDA0004032244440000051

其中

Figure BDA0004032244440000052
分别表示左上,右上,左下,右下四个点的像素坐标。in
Figure BDA0004032244440000052
Represents the pixel coordinates of the upper left, upper right, lower left, and lower right points respectively.

获取视频目标框的中心位置Ut,结合相机标定系统,获取视频目标对应的实际空间位置信息Xt,即:Get the center position U t of the video target frame, and combine it with the camera calibration system to get the actual spatial position information X t corresponding to the video target, that is:

Figure BDA0004032244440000061
Figure BDA0004032244440000061

其中,f(x)为相机标定系统。Among them, f(x) is the camera calibration system.

在一个可选的实施例中,在所述将所述目标车辆在所述第二时刻的位置信息确定为所述第一车辆的位置信息,所述方法还包括:根据所述目标车辆在所述第二时刻的位置信息确定所述目标车辆是否到达预设位置;在所述目标车辆到达所述预设位置的情况下,将所述目标车辆添加到目标车辆集合中,并确定所述目标车辆的到达时间为所述第二时刻,其中,所述目标车辆集合记录了到达过所述预设位置的车辆以及每个车辆对应的到达时间;将所述目标车辆集合中在预设时间段内到达所述预设位置的车辆的数量确定预设时间段内的车流量。In an optional embodiment, in determining the position information of the target vehicle at the second moment as the position information of the first vehicle, the method further includes: determining whether the target vehicle has arrived at a preset position based on the position information of the target vehicle at the second moment; when the target vehicle arrives at the preset position, adding the target vehicle to a target vehicle set, and determining the arrival time of the target vehicle as the second moment, wherein the target vehicle set records the vehicles that have arrived at the preset position and the arrival time corresponding to each vehicle; and determining the traffic flow within the preset time period by the number of vehicles in the target vehicle set that arrive at the preset position within the preset time period.

在本实施例中,在目标车辆在第二时刻的位置信息确定之后,根据所述目标车辆在第二时刻的位置信息去确定目标车辆是否到达预设位置,在达到预设位置的情况下,将目标车辆添加到目标车辆集合中,并记录下目标车辆到达预设位置的时间。In this embodiment, after the position information of the target vehicle at the second moment is determined, whether the target vehicle has reached the preset position is determined based on the position information of the target vehicle at the second moment. If the target vehicle has reached the preset position, the target vehicle is added to the target vehicle set, and the time when the target vehicle arrives at the preset position is recorded.

其中,预设位置可以是设置在道路上的一条线,通过统计跨过这条线的车辆的数量,进而统计车流量,通过上述目标车辆集合中车辆的数量统计车流量不会重复计数,保证了车流量统计的准确性。Among them, the preset position can be a line set on the road. The number of vehicles crossing this line is counted to count the traffic flow. The traffic flow counted by the number of vehicles in the above target vehicle set will not be counted repeatedly, thereby ensuring the accuracy of the traffic flow statistics.

在统计预设时间段内的车流量时,通过确定在目标车辆集合中到达预设位置的时间在预设时间段内的车辆的数量,确定出预设时间段内的车流量。When counting the traffic volume in a preset time period, the traffic volume in the preset time period is determined by determining the number of vehicles in the target vehicle set that arrive at the preset position within the preset time period.

可选的,可以通过以下公式确定目标车辆是否达到预设位置:Optionally, the following formula can be used to determine whether the target vehicle has reached the preset position:

Figure BDA0004032244440000062
Figure BDA0004032244440000062

其中,c为预设位置的位置值,

Figure BDA0004032244440000063
为目标车辆在所述第二时刻的位置信息。
Figure BDA0004032244440000064
则判定目标车辆达到预设位置。Where c is the position value of the preset position,
Figure BDA0004032244440000063
is the position information of the target vehicle at the second moment.
Figure BDA0004032244440000064
It is determined that the target vehicle reaches the preset position.

在一个可选的实施例中,所述根据目标车辆在第一时刻的位置信息,确定所述目标车辆在第二时刻的预测位置信息,包括:获取在第一图像中用于标识所述目标车辆的第一目标框,其中,所述第一图像是通过目标拍摄设备在所述第一时刻拍摄得到的图像;根据所述第一目标框的坐标信息确定所述目标车辆在所述第一时刻的位置信息;将所述目标车辆在所述第一时刻的位置信息输入至目标预测模型,得到所述目标车辆在所述第二时刻的所述预测位置信息。In an optional embodiment, determining the predicted position information of the target vehicle at a second moment based on the position information of the target vehicle at a first moment includes: obtaining a first target frame for identifying the target vehicle in a first image, wherein the first image is an image captured by a target shooting device at the first moment; determining the position information of the target vehicle at the first moment based on coordinate information of the first target frame; and inputting the position information of the target vehicle at the first moment into a target prediction model to obtain the predicted position information of the target vehicle at the second moment.

在本实施例中,在预测目标车辆在第二时刻的位置信息时,首先确定第一时刻上目标车辆的位置信息:目标拍摄设备被固定在固定位置,通过固定位置拍摄图像,在第一时刻拍摄得到的第一图像中识别出目标车辆,并在第一图像中通过第一目标框标识目标车辆,通过目标框在第一图像中的坐标信息确定目标车辆在第一时刻的位置信息,将目标车辆在第一时刻的位置信息输入至目标预测模型,通过预测模型进行位置的预测,得到所述目标车辆在第二时刻的预测位置信息。In this embodiment, when predicting the position information of the target vehicle at the second moment, the position information of the target vehicle at the first moment is first determined: the target shooting device is fixed at a fixed position, and the image is shot by the fixed position. The target vehicle is identified in the first image shot at the first moment, and the target vehicle is identified by a first target frame in the first image. The position information of the target vehicle at the first moment is determined by the coordinate information of the target frame in the first image, and the position information of the target vehicle at the first moment is input into the target prediction model. The position is predicted by the prediction model to obtain the predicted position information of the target vehicle at the second moment.

需要说明的是,在目标拍摄设备被固定之后,通过目标拍摄设备中的标定系统进行标定,使得目标拍摄设备拍摄的图像中的像素点坐标可以映射到实际空间中的位置。It should be noted that after the target shooting device is fixed, calibration is performed through a calibration system in the target shooting device so that the coordinates of the pixel points in the image shot by the target shooting device can be mapped to positions in the actual space.

通过输入目标车辆在第一时刻的位置信息x(k-1),目标预测模型输出目标车辆在第二时刻的预测位置信息x(k)。By inputting the position information x(k-1) of the target vehicle at the first moment, the target prediction model outputs the predicted position information x(k) of the target vehicle at the second moment.

上述目标预测模型可以是卡尔曼滤波算法模型,卡尔曼滤波算法根据系统的状态模型和观测模型,实时跟踪预测的目标位置信息,在本实施例中应选用离散动态系统,由q维动态系统和r维观测系统组成。其中,q维动态系统的状态模型为:The target prediction model can be a Kalman filter algorithm model. The Kalman filter algorithm tracks the predicted target position information in real time according to the state model and observation model of the system. In this embodiment, a discrete dynamic system should be selected, which is composed of a q-dimensional dynamic system and an r-dimensional observation system. Among them, the state model of the q-dimensional dynamic system is:

x(k)=Ax(k-1)+w(k-1)x(k)=Ax(k-1)+w(k-1)

其中w(k)为状态模型误差。Where w(k) is the state model error.

w(k)的协方差矩阵为:The covariance matrix of w(k) is:

Q(k)=E[w(k)w(k)T]Q(k)=E[w(k)w(k) T ]

r维观测系统的观测模型为:The observation model of the r-dimensional observation system is:

y(k)=F(k)+v(k)y(k)=F(k)+v(k)

其中F(k)为观测系统模型,v(k)为观测系统模型误差,v(k)的协方差矩阵为:Where F(k) is the observation system model, v(k) is the observation system model error, and the covariance matrix of v(k) is:

R(k)=E[v(k)v(k)T]R(k)=E[v(k)v(k) T ]

卡尔曼滤波的协方差预测方程:Kalman filter covariance prediction equation:

P1(k)=AP(k-1)AT+Q(k)P 1 (k) = AP (k-1) AT + Q (k)

滤波增益方程:Filter gain equation:

K(k)=P1(k)CT[CP1(k)CT+R(k)]-1 K(k)=P 1 (k)C T [CP 1 (k)C T +R(k)] -1

滤波协方差方程:Filter covariance equation:

P(x)=P1(k)-K(k)CP1(k)P(x)=P 1 (k)-K(k)CP 1 (k)

滤波估计方程:Filter estimation equation:

Figure BDA0004032244440000081
Figure BDA0004032244440000081

预测估计方程:The prediction estimation equation is:

Figure BDA0004032244440000082
Figure BDA0004032244440000082

卡尔曼滤波模型中的状态向量x(k):The state vector x(k) in the Kalman filter model is:

x(k)=[x,y,vx,vy]x(k)=[x,y,v x ,v y ]

量测向量y(k):Measurement vector y(k):

y(k)=[x,y]T y(k)=[x ,y ] T

系统参数为A:The system parameters are A:

Figure BDA0004032244440000083
Figure BDA0004032244440000083

在一个可选的实施例中,所述获取在所述第二时刻位于目标范围内的车辆,得到第一车辆集合,包括:获取第二目标框集合,其中,所述第二目标框集合中的目标框与第二车辆集合中的车辆一一对应,所述第二目标框集合包括第二图像中用于标识所述第二车辆集合中各个车辆的目标框,所述第二车辆集合包括在所述第二图像中识别的车辆,所述第二图像是通过目标拍摄设备在所述第二时刻拍摄得到的图像;根据所述第二目标框集合中各个目标框的坐标信息确定在所述第二车辆集合中的各个车辆在所述第二时刻的位置信息;在所述第二车辆集合中查找在所述第二时刻的位置信息位于所述目标范围内的车辆,并将查找出的车辆确定为所述第一车辆集合。In an optional embodiment, the acquiring of vehicles located within the target range at the second moment to obtain the first vehicle set includes: acquiring a second target frame set, wherein the target frames in the second target frame set correspond one-to-one to the vehicles in the second vehicle set, the second target frame set includes a target frame in a second image for identifying each vehicle in the second vehicle set, the second vehicle set includes vehicles identified in the second image, and the second image is an image captured by a target shooting device at the second moment; determining the position information of each vehicle in the second vehicle set at the second moment according to the coordinate information of each target frame in the second target frame set; searching the second vehicle set for vehicles whose position information at the second moment is within the target range, and determining the found vehicles as the first vehicle set.

在本实施例中,在目标拍摄设备在第二时刻拍摄的第二图像中识别出的所有车辆组成第二车辆集合,并在第二图像中使用目标框进行标识每个车辆,第二图像中的所有的目标框组成第二目标框集合,通过第二目标框集合中的各个目标框在第二图像中的坐标信息确定在第二车辆集合中的各个车辆在第二时刻的位置信息,然后根据第二车辆集合中的各个车辆在第二时刻的位置信息,在第二车辆集合中确定出在第二时刻的位置信息位于所述目标范围内的车辆,确定为第一车辆集合。In this embodiment, all vehicles identified in the second image taken by the target shooting device at the second moment constitute a second vehicle set, and a target box is used to identify each vehicle in the second image. All target boxes in the second image constitute a second target box set, and the position information of each vehicle in the second vehicle set at the second moment is determined by the coordinate information of each target box in the second target box set in the second image. Then, based on the position information of each vehicle in the second vehicle set at the second moment, the vehicles whose position information at the second moment is within the target range are determined in the second vehicle set and determined as the first vehicle set.

图4是根据本发明实施例的确定第一车辆集合的示意图,如图4所示,在第二图像中识别出了五辆车辆(分别标号为1、2、3、4、5),五辆车辆在第二时刻的位置信息分别为x2,1,x2,2,x2,3,x2,4,x2,5,目标范围如图4中虚线圆圈所示,其中车辆2和车辆3在目标范围之内,则第一车辆集合包括车辆1和车辆2。Figure 4 is a schematic diagram of determining the first vehicle set according to an embodiment of the present invention. As shown in Figure 4, five vehicles (labeled 1, 2, 3, 4, and 5 respectively) are identified in the second image, and the position information of the five vehicles at the second moment is x2,1 , x2,2 , x2,3 , x2,4 , and x2,5 respectively. The target range is shown as the dotted circle in Figure 4, where vehicle 2 and vehicle 3 are within the target range, and the first vehicle set includes vehicle 1 and vehicle 2.

在一个可选的实施例中,所述在所述第一车辆集合中确定与所述目标车辆匹配的第一车辆,包括:确定所述第一车辆集合中各个车辆与所述目标车辆的匹配关系损失值,其中,所述匹配关系损失值用于表示所述第一车辆集合中各个车辆与所述目标车辆匹配的误差;将所述第一车辆集合中匹配关系损失值最小的车辆确定为与所述目标车辆匹配的所述第一车辆。In an optional embodiment, determining the first vehicle that matches the target vehicle in the first vehicle set includes: determining a matching relationship loss value between each vehicle in the first vehicle set and the target vehicle, wherein the matching relationship loss value is used to represent the error between the matching of each vehicle in the first vehicle set and the target vehicle; and determining the vehicle with the smallest matching relationship loss value in the first vehicle set as the first vehicle that matches the target vehicle.

在本实施例中,将目标车辆与第一车辆集合中的各个车辆进行匹配操作,并计算匹配关系损失值,将损失值最小的车辆确定为与目标车辆匹配的车辆。In this embodiment, the target vehicle is matched with each vehicle in the first vehicle set, and a matching relationship loss value is calculated, and the vehicle with the smallest loss value is determined as the vehicle matching the target vehicle.

如图3中的第一车辆集合包括车辆1和车辆2,分别计算车辆1和目标车辆的匹配关系损失值,假如为0.2,计算车辆2和目标车辆的匹配关系损失值,假如为0.1,则车辆2与和目标车辆的匹配关系损失值较小,则将车辆2与目标车辆匹配,即车辆2为目标车辆。As shown in Figure 3, the first vehicle set includes vehicle 1 and vehicle 2. The matching relationship loss value between vehicle 1 and the target vehicle is calculated respectively. If it is 0.2, the matching relationship loss value between vehicle 2 and the target vehicle is calculated. If it is 0.1, the matching relationship loss value between vehicle 2 and the target vehicle is smaller, then vehicle 2 is matched with the target vehicle, that is, vehicle 2 is the target vehicle.

在一个可选的实施例中,所述确定所述第一车辆集合中各个车辆与所述目标车辆的匹配关系损失值,包括:针对所述第一车辆集合中的每个车辆执行以下操作,在执行以下操作时,所述第一车辆集合中的每个车辆为当前车辆:根据所述当前车辆在所述第二时刻的位置信息和所述目标车辆在第二时刻的预测位置信息,确定第一损失值,其中,所述第一损失值代表所述当前车辆与所述目标车辆之间的质心损失值;根据所述第二图像中用于标识所述当前车辆的当前目标框与第一目标框,确定第二损失值,其中,所述第一目标框为第一图像中用于标识所述目标车辆的目标框,所述第一图像是通过所述目标拍摄设备在所述第一时刻拍摄得到的图像,所述第一损失值代表所述当前目标框与所述第一目标框之间的重叠面积损失值;根据所述当前目标框与所述第一目标框,确定第三损失值,其中,所述第一损失值代表所述当前车辆与所述目标车辆之间的面积相似损失值;根据所述第一损失值、所述第二损失值和所述第三损失值确定所述当前车辆与所述目标车辆的匹配关系损失值。In an optional embodiment, determining the matching relationship loss value between each vehicle in the first vehicle set and the target vehicle includes: performing the following operations for each vehicle in the first vehicle set, when performing the following operations, each vehicle in the first vehicle set is the current vehicle: determining a first loss value according to the position information of the current vehicle at the second moment and the predicted position information of the target vehicle at the second moment, wherein the first loss value represents the center of mass loss value between the current vehicle and the target vehicle; determining a second loss value according to a current target frame and a first target frame used to identify the current vehicle in the second image, wherein the first target frame is a target frame used to identify the target vehicle in the first image, the first image is an image captured by the target capturing device at the first moment, and the first loss value represents the overlapping area loss value between the current target frame and the first target frame; determining a third loss value according to the current target frame and the first target frame, wherein the first loss value represents the area similarity loss value between the current vehicle and the target vehicle; determining the matching relationship loss value between the current vehicle and the target vehicle according to the first loss value, the second loss value and the third loss value.

在本实施例中,通过三个不同纬度的损失值确定匹配关系损失值,包括:第一损失值,第二损失值,第三损失值,其中第一损失值代表质心损失值,第二损失值代表重叠面积损失值,第三损失值代表面积相似损失值;In this embodiment, the matching relationship loss value is determined by loss values of three different latitudes, including: a first loss value, a second loss value, and a third loss value, wherein the first loss value represents a centroid loss value, the second loss value represents an overlapping area loss value, and the third loss value represents an area similarity loss value;

将第一损失值,第二损失值,第三损失值进行归一化处理:Normalize the first loss value, the second loss value, and the third loss value:

D(n,m)=D(n,m)/maxD(n,*)D(n,m)=D(n,m)/maxD(n,*)

L(n,m)=L(n,m)/maxL(n,*)L(n,m)=L(n,m)/maxL(n,*)

ΔS(n,m)=ΔS(n,m)/maxΔS(n,*)ΔS(n,m)=ΔS(n,m)/maxΔS(n,*)

其中,n表示目标车辆,m表示车辆中的一个车辆,*为第一车辆集合中所有车辆,D(n,m)为第一损失值,L(n,m)为第二损失值,ΔS(n,m)为第三损失值。Among them, n represents the target vehicle, m represents a vehicle in the vehicle, * represents all vehicles in the first vehicle set, D(n,m) is the first loss value, L(n,m) is the second loss value, and ΔS(n,m) is the third loss value.

匹配关系损失值为:The matching relationship loss value is:

loss(n,m)=αD(n,m)+βL(n,m)+γΔS(n,m)loss(n,m)=αD(n,m)+βL(n,m)+γΔS(n,m)

其中,α、β、γ均为预设值。Among them, α, β, and γ are all preset values.

在一个可选的实施例中,所述根据所述第二图像中用于标识所述当前车辆的当前目标框与第一目标框,确定第二损失值,包括:确定所述当前目标框与所述第一目标框的重叠面积以及所述第一目标框的第一面积;确定所述重叠面积和所述第一面积的比值;将所述比值与1的差值确定为所述第二损失值。In an optional embodiment, determining the second loss value based on the current target frame and the first target frame used to identify the current vehicle in the second image includes: determining the overlapping area between the current target frame and the first target frame and the first area of the first target frame; determining the ratio of the overlapping area to the first area; and determining the difference between the ratio and 1 as the second loss value.

在本实施例中,通过当前目标框和第一目标框计算第二损失值:In this embodiment, the second loss value is calculated by the current target frame and the first target frame:

Figure BDA0004032244440000111
Figure BDA0004032244440000111

其中,S为当前目标框与所述第一目标框的重叠面积,

Figure BDA0004032244440000112
为第一目标框的第一面积。Wherein, S is the overlapping area between the current target frame and the first target frame,
Figure BDA0004032244440000112
is the first area of the first target box.

在一个可选的实施例中,所述根据所述当前目标框与所述第一目标框,确定第三损失值,包括:确定所述当前目标框的第二面积以及所述第一目标框的第一面积;根据所述第一面积和所述第二面积之间的差值确定为所述第三损失值。In an optional embodiment, determining the third loss value based on the current target box and the first target box includes: determining the second area of the current target box and the first area of the first target box; and determining the third loss value based on the difference between the first area and the second area.

在本实施例中,通过当前目标框与所述第一目标框确定为第三损失值:In this embodiment, the third loss value is determined by the current target frame and the first target frame:

Figure BDA0004032244440000113
Figure BDA0004032244440000113

其中,

Figure BDA0004032244440000114
为当前目标框的第二面积,
Figure BDA0004032244440000115
为第一目标框的第一面积。in,
Figure BDA0004032244440000114
is the second area of the current target box,
Figure BDA0004032244440000115
is the first area of the first target box.

可选的,通过当前车辆在所述第二时刻的位置信息和所述目标车辆在第二时刻的预测位置信息确定第一损失值:Optionally, the first loss value is determined by using the position information of the current vehicle at the second moment and the predicted position information of the target vehicle at the second moment:

Figure BDA0004032244440000116
Figure BDA0004032244440000116

其中,

Figure BDA0004032244440000117
为当前车辆在所述第二时刻的位置信息,
Figure BDA0004032244440000118
为目标车辆在第二时刻的预测位置信息。in,
Figure BDA0004032244440000117
is the position information of the current vehicle at the second moment,
Figure BDA0004032244440000118
is the predicted position information of the target vehicle at the second moment.

显然,上述所描述的实施例仅仅是本发明一部分的实施例,而不是全部的实施例。Obviously, the above-described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

下面结合实施例对本发明进行具体说明:The present invention will be specifically described below in conjunction with embodiments:

图5是根据本发明具体实施例的车辆的位置更新的流程示意图,如图5所示,包括:FIG5 is a schematic diagram of a process of updating the position of a vehicle according to a specific embodiment of the present invention, as shown in FIG5 , including:

步骤1:开始。程序启动。跳转步骤2。Step 1: Start. The program starts. Go to step 2.

步骤2:初始化。设置预设位置、目标检测区域等,跳转步骤3。Step 2: Initialization. Set the preset position, target detection area, etc., and jump to step 3.

步骤3:相机标定,该步骤主要解决视频像素值与实际空间距离的对应关系,视频标定精度影响后续车流量统计的准确率。跳转步骤4。Step 3: Camera calibration. This step mainly solves the correspondence between the video pixel value and the actual space distance. The accuracy of video calibration affects the accuracy of subsequent vehicle flow statistics. Jump to step 4.

步骤4:视频目标车辆检测。采用深度学习等人工智能方法,检测目标车辆的个数和位置,并赋予每一个目标车辆唯一的ID。跳转步骤5。Step 4: Video target vehicle detection. Use artificial intelligence methods such as deep learning to detect the number and location of target vehicles and assign a unique ID to each target vehicle. Go to step 5.

步骤5:视频目标车辆跟踪。该步骤的目的是同一个目标车辆拥有一个稳定的ID。跳转步骤6。Step 5: Video target vehicle tracking. The purpose of this step is to have a stable ID for the same target vehicle. Go to step 6.

步骤6:提取目标框位置点。基于视频中目标框位置,提取目标框中心点。并通过标定算法将目标框中心点映射为世界坐标系下的实际位置,跳转步骤7。Step 6: Extract the target frame position point. Based on the target frame position in the video, extract the target frame center point. And map the target frame center point to the actual position in the world coordinate system through the calibration algorithm, and jump to step 7.

步骤7:根据目标实际位置信息获取位于目标检测区域的目标车辆个数和状态信息。跳转步骤8。Step 7: Obtain the number and status information of target vehicles in the target detection area according to the actual position information of the target. Go to step 8.

步骤8:基于卡尔曼滤波算法进行目标车辆预测更新。跳转步骤9。Step 8: Update the target vehicle prediction based on the Kalman filter algorithm. Go to step 9.

步骤9:基于预设范围获取第一车辆集合。跳转步骤10。Step 9: Obtain a first vehicle set based on a preset range. Go to step 10.

步骤10:计算损失值。针对第一车辆集合中每个车辆,计算与目标车辆的损失值。跳转步骤11。Step 10: Calculate the loss value. For each vehicle in the first vehicle set, calculate the loss value with respect to the target vehicle. Jump to step 11.

步骤11:选择最佳匹配。从所有损失值中,选择损失值最小对应的车辆确定为目标车辆。跳转步骤12。Step 11: Select the best match. From all loss values, select the vehicle with the smallest loss value as the target vehicle. Go to step 12.

步骤12:判断是否到达预设位置,进行车流量统计。在车辆未到达预设位置的情况下,跳转至第8步进行目标状态进一步预测,否组跳转至步骤13。Step 12: Determine whether the vehicle has reached the preset location and perform vehicle flow statistics. If the vehicle has not reached the preset location, jump to step 8 to further predict the target state. If not, jump to step 13.

步骤13:结束。Step 13: End.

通过以上的实施方式的描述,本领域的技术人员可以清楚地了解到根据上述实施例的方法可借助软件加必需的通用硬件平台的方式来实现,当然也可以通过硬件,但很多情况下前者是更佳的实施方式。基于这样的理解,本发明的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中,包括若干指令用以使得一台终端设备(可以是手机,计算机,服务器,或者网络设备等)执行本发明各个实施例所述的方法。Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

在本实施例中还提供了一种车辆的位置更新装置,图6是根据本发明实施例的车辆的位置更新装置的结构框图,如图6所示,该装置包括:In this embodiment, a vehicle position updating device is also provided. FIG. 6 is a structural block diagram of a vehicle position updating device according to an embodiment of the present invention. As shown in FIG. 6 , the device includes:

第一确定模块602,用于根据目标车辆在第一时刻的位置信息,确定所述目标车辆在第二时刻的预测位置信息,其中,所述第二时刻晚于所述第一时刻;A first determining module 602, configured to determine predicted position information of the target vehicle at a second moment according to the position information of the target vehicle at a first moment, wherein the second moment is later than the first moment;

获取模块604,用于获取在所述第二时刻位于目标范围内的车辆,得到第一车辆集合,其中,所述目标范围是根据所述目标车辆在所述第一时刻的位置信息确定的;An acquisition module 604 is used to acquire vehicles located within a target range at the second moment to obtain a first vehicle set, wherein the target range is determined according to the position information of the target vehicles at the first moment;

第二确定模块606,用于根据所述预测位置信息在所述第一车辆集合中确定与所述目标车辆匹配的第一车辆,并将所述第一车辆的位置信息确定为所述目标车辆在所述第二时刻的位置信息。The second determination module 606 is used to determine a first vehicle matching the target vehicle in the first vehicle set according to the predicted position information, and determine the position information of the first vehicle as the position information of the target vehicle at the second moment.

在一个可选的实施例中,上述装置还用于,根据所述目标车辆在所述第二时刻的位置信息确定所述目标车辆是否到达预设位置;在所述目标车辆到达所述预设位置的情况下,将所述目标车辆添加到目标车辆集合中,并确定所述目标车辆的到达时间为所述第二时刻,其中,所述目标车辆集合记录了到达过所述预设位置的车辆以及每个车辆对应的到达时间;将所述目标车辆集合中在预设时间段内到达所述预设位置的车辆的数量确定预设时间段内的车流量。In an optional embodiment, the above-mentioned device is also used to determine whether the target vehicle has arrived at the preset position based on the position information of the target vehicle at the second moment; when the target vehicle arrives at the preset position, the target vehicle is added to the target vehicle set, and the arrival time of the target vehicle is determined to be the second moment, wherein the target vehicle set records the vehicles that have arrived at the preset position and the corresponding arrival time of each vehicle; the number of vehicles in the target vehicle set that arrive at the preset position within the preset time period is used to determine the traffic flow within the preset time period.

在一个可选的实施例中,上述装置还用于,获取在第一图像中用于标识所述目标车辆的第一目标框,其中,所述第一图像是通过目标拍摄设备在所述第一时刻拍摄得到的图像;根据所述第一目标框的坐标信息确定所述目标车辆在所述第一时刻的位置信息;将所述目标车辆在所述第一时刻的位置信息输入至目标预测模型,得到所述目标车辆在所述第二时刻的所述预测位置信息。In an optional embodiment, the above-mentioned device is also used to obtain a first target frame for identifying the target vehicle in a first image, wherein the first image is an image captured by a target shooting device at the first moment; determine the position information of the target vehicle at the first moment based on the coordinate information of the first target frame; input the position information of the target vehicle at the first moment into a target prediction model to obtain the predicted position information of the target vehicle at the second moment.

在一个可选的实施例中,上述装置还用于,获取第二目标框集合,其中,所述第二目标框集合中的目标框与第二车辆集合中的车辆一一对应,所述第二目标框集合包括第二图像中用于标识所述第二车辆集合中各个车辆的目标框,所述第二车辆集合包括在所述第二图像中识别的车辆,所述第二图像是通过目标拍摄设备在所述第二时刻拍摄得到的图像;根据所述第二目标框集合中各个目标框的坐标信息确定在所述第二车辆集合中的各个车辆在所述第二时刻的位置信息;在所述第二车辆集合中查找在所述第二时刻的位置信息位于所述目标范围内的车辆,并将查找出的车辆确定为所述第一车辆集合。In an optional embodiment, the above-mentioned device is also used to obtain a second target frame set, wherein the target frames in the second target frame set correspond one-to-one to the vehicles in the second vehicle set, the second target frame set includes target frames in the second image for identifying each vehicle in the second vehicle set, the second vehicle set includes vehicles identified in the second image, and the second image is an image captured by a target shooting device at the second moment; determine the position information of each vehicle in the second vehicle set at the second moment according to the coordinate information of each target frame in the second target frame set; search for vehicles in the second vehicle set whose position information at the second moment is within the target range, and determine the found vehicles as the first vehicle set.

在一个可选的实施例中,上述装置还用于,确定所述第一车辆集合中各个车辆与所述目标车辆的匹配关系损失值,其中,所述匹配关系损失值用于表示所述第一车辆集合中各个车辆与所述目标车辆匹配的误差;将所述第一车辆集合中匹配关系损失值最小的车辆确定为与所述目标车辆匹配的所述第一车辆。In an optional embodiment, the above-mentioned device is also used to determine the matching relationship loss value between each vehicle in the first vehicle set and the target vehicle, wherein the matching relationship loss value is used to represent the matching error between each vehicle in the first vehicle set and the target vehicle; and the vehicle with the smallest matching relationship loss value in the first vehicle set is determined as the first vehicle that matches the target vehicle.

在一个可选的实施例中,上述装置还用于,针对所述第一车辆集合中的每个车辆执行以下操作,在执行以下操作时,所述第一车辆集合中的每个车辆为当前车辆:根据所述当前车辆在所述第二时刻的位置信息和所述目标车辆在第二时刻的预测位置信息,确定第一损失值,其中,所述第一损失值代表所述当前车辆与所述目标车辆之间的质心损失值;根据所述第二图像中用于标识所述当前车辆的当前目标框与第一目标框,确定第二损失值,其中,所述第一目标框为第一图像中用于标识所述目标车辆的目标框,所述第一图像是通过所述目标拍摄设备在所述第一时刻拍摄得到的图像,所述第一损失值代表所述当前目标框与所述第一目标框之间的重叠面积损失值;根据所述当前目标框与所述第一目标框,确定第三损失值,其中,所述第一损失值代表所述当前车辆与所述目标车辆之间的面积相似损失值;根据所述第一损失值、所述第二损失值和所述第三损失值确定所述当前车辆与所述目标车辆的匹配关系损失值。In an optional embodiment, the above-mentioned device is also used to perform the following operations for each vehicle in the first vehicle set, and when performing the following operations, each vehicle in the first vehicle set is the current vehicle: determine a first loss value based on the position information of the current vehicle at the second moment and the predicted position information of the target vehicle at the second moment, wherein the first loss value represents the center of mass loss value between the current vehicle and the target vehicle; determine a second loss value based on the current target frame and the first target frame used to identify the current vehicle in the second image, wherein the first target frame is the target frame used to identify the target vehicle in the first image, and the first image is an image captured by the target shooting device at the first moment, and the first loss value represents the overlapping area loss value between the current target frame and the first target frame; determine a third loss value based on the current target frame and the first target frame, wherein the first loss value represents the area similarity loss value between the current vehicle and the target vehicle; determine the matching relationship loss value between the current vehicle and the target vehicle based on the first loss value, the second loss value and the third loss value.

在一个可选的实施例中,上述装置还用于,确定所述当前目标框与所述第一目标框的重叠面积以及所述第一目标框的第一面积;确定所述重叠面积和所述第一面积的比值;将所述比值与1的差值确定为所述第二损失值。In an optional embodiment, the above-mentioned device is also used to determine the overlapping area between the current target frame and the first target frame and the first area of the first target frame; determine the ratio of the overlapping area to the first area; and determine the difference between the ratio and 1 as the second loss value.

在一个可选的实施例中,上述装置还用于,确定所述当前目标框的第二面积以及所述第一目标框的第一面积;根据所述第一面积和所述第二面积之间的差值确定为所述第三损失值。In an optional embodiment, the above-mentioned device is also used to determine the second area of the current target frame and the first area of the first target frame; and determine the third loss value according to the difference between the first area and the second area.

需要说明的是,上述各个模块是可以通过软件或硬件来实现的,对于后者,可以通过以下方式实现,但不限于此:上述模块均位于同一处理器中;或者,上述各个模块以任意组合的形式分别位于不同的处理器中。It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

本发明的实施例还提供了一种计算机可读存储介质,该计算机可读存储介质中存储有计算机程序,其中,该计算机程序被设置为运行时执行上述任一项方法实施例中的步骤。An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

在一个示例性实施例中,上述计算机可读存储介质可以包括但不限于:U盘、只读存储器(Read-Only Memory,简称为ROM)、随机存取存储器(Random Access Memory,简称为RAM)、移动硬盘、磁碟或者光盘等各种可以存储计算机程序的介质。In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

本发明的实施例还提供了一种电子装置,包括存储器和处理器,该存储器中存储有计算机程序,该处理器被设置为运行计算机程序以执行上述任一项方法实施例中的步骤。An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

在一个示例性实施例中,上述电子装置还可以包括传输设备以及输入输出设备,其中,该传输设备和上述处理器连接,该输入输出设备和上述处理器连接。In an exemplary embodiment, the electronic device may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.

本实施例中的具体示例可以参考上述实施例及示例性实施方式中所描述的示例,本实施例在此不再赘述。For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

显然,本领域的技术人员应该明白,上述的本发明的各模块或各步骤可以用通用的计算装置来实现,它们可以集中在单个的计算装置上,或者分布在多个计算装置所组成的网络上,它们可以用计算装置可执行的程序代码来实现,从而,可以将它们存储在存储装置中由计算装置来执行,并且在某些情况下,可以以不同于此处的顺序执行所示出或描述的步骤,或者将它们分别制作成各个集成电路模块,或者将它们中的多个模块或步骤制作成单个集成电路模块来实现。这样,本发明不限制于任何特定的硬件和软件结合。Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

以上所述仅为本发明的优选实施例而已,并不用于限制本发明,对于本领域的技术人员来说,本发明可以有各种更改和变化。凡在本发明的原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims (10)

1.一种车辆的位置更新方法,其特征在于,包括:1. A method for updating the position of a vehicle, comprising: 根据目标车辆在第一时刻的位置信息,确定所述目标车辆在第二时刻的预测位置信息,其中,所述第二时刻晚于所述第一时刻;Determining predicted position information of the target vehicle at a second moment according to the position information of the target vehicle at a first moment, wherein the second moment is later than the first moment; 获取在所述第二时刻位于目标范围内的车辆,得到第一车辆集合,其中,所述目标范围是根据所述目标车辆在所述第一时刻的位置信息确定的;Acquire vehicles located within a target range at the second moment to obtain a first vehicle set, wherein the target range is determined according to position information of the target vehicles at the first moment; 根据所述预测位置信息在所述第一车辆集合中确定与所述目标车辆匹配的第一车辆,并将所述第一车辆的位置信息确定为所述目标车辆在所述第二时刻的位置信息。A first vehicle matching the target vehicle is determined in the first vehicle set according to the predicted position information, and the position information of the first vehicle is determined as the position information of the target vehicle at the second moment. 2.根据权利要求1所述的方法,其特征在于,在所述将所述目标车辆在所述第二时刻的位置信息确定为所述第一车辆的位置信息之后,所述方法还包括:2. The method according to claim 1, characterized in that, after determining the position information of the target vehicle at the second moment as the position information of the first vehicle, the method further comprises: 根据所述目标车辆在所述第二时刻的位置信息确定所述目标车辆是否到达预设位置;Determining whether the target vehicle has reached a preset position according to the position information of the target vehicle at the second moment; 在所述目标车辆到达所述预设位置的情况下,将所述目标车辆添加到目标车辆集合中,并确定所述目标车辆的到达时间为所述第二时刻,其中,所述目标车辆集合记录了到达过所述预设位置的车辆以及每个车辆对应的到达时间;When the target vehicle arrives at the preset position, the target vehicle is added to a target vehicle set, and the arrival time of the target vehicle is determined to be the second moment, wherein the target vehicle set records the vehicles that have arrived at the preset position and the arrival time corresponding to each vehicle; 将所述目标车辆集合中在预设时间段内到达所述预设位置的车辆的数量确定预设时间段内的车流量。The vehicle flow rate within the preset time period is determined by the number of vehicles in the target vehicle set that arrive at the preset position within the preset time period. 3.根据权利要求1所述的方法,其特征在于,所述根据目标车辆在第一时刻的位置信息,确定所述目标车辆在第二时刻的预测位置信息,包括:3. The method according to claim 1, characterized in that the step of determining the predicted position information of the target vehicle at the second moment based on the position information of the target vehicle at the first moment comprises: 获取在第一图像中用于标识所述目标车辆的第一目标框,其中,所述第一图像是通过目标拍摄设备在所述第一时刻拍摄得到的图像;Acquire a first target frame for identifying the target vehicle in a first image, wherein the first image is an image captured by a target capturing device at the first moment; 根据所述第一目标框的坐标信息确定所述目标车辆在所述第一时刻的位置信息;Determining the position information of the target vehicle at the first moment according to the coordinate information of the first target frame; 将所述目标车辆在所述第一时刻的位置信息输入至目标预测模型,得到所述目标车辆在所述第二时刻的所述预测位置信息。The position information of the target vehicle at the first moment is input into a target prediction model to obtain the predicted position information of the target vehicle at the second moment. 4.根据权利要求1所述的方法,其特征在于,所述获取在所述第二时刻位于目标范围内的车辆,得到第一车辆集合,包括:4. The method according to claim 1, characterized in that the acquiring of the vehicles located within the target range at the second moment to obtain the first vehicle set comprises: 获取第二目标框集合,其中,所述第二目标框集合中的目标框与第二车辆集合中的车辆一一对应,所述第二目标框集合包括第二图像中用于标识所述第二车辆集合中各个车辆的目标框,所述第二车辆集合包括在所述第二图像中识别的车辆,所述第二图像是通过目标拍摄设备在所述第二时刻拍摄得到的图像;Acquire a second target frame set, wherein the target frames in the second target frame set correspond one-to-one to the vehicles in the second vehicle set, the second target frame set includes target frames in the second image for identifying each vehicle in the second vehicle set, the second vehicle set includes vehicles identified in the second image, and the second image is an image captured by the target capturing device at the second moment; 根据所述第二目标框集合中各个目标框的坐标信息确定在所述第二车辆集合中的各个车辆在所述第二时刻的位置信息;Determining the position information of each vehicle in the second vehicle set at the second moment according to the coordinate information of each target frame in the second target frame set; 在所述第二车辆集合中查找在所述第二时刻的位置信息位于所述目标范围内的车辆,并将查找出的车辆确定为所述第一车辆集合。The second vehicle set is searched for vehicles whose position information at the second moment is within the target range, and the searched vehicles are determined as the first vehicle set. 5.根据权利要求4所述的方法,其特征在于,所述在所述第一车辆集合中确定与所述目标车辆匹配的第一车辆,包括:5. The method according to claim 4, characterized in that the determining the first vehicle matching the target vehicle in the first vehicle set comprises: 确定所述第一车辆集合中各个车辆与所述目标车辆的匹配关系损失值,其中,所述匹配关系损失值用于表示所述第一车辆集合中各个车辆与所述目标车辆匹配的误差;Determine a matching relationship loss value between each vehicle in the first vehicle set and the target vehicle, wherein the matching relationship loss value is used to represent an error in matching each vehicle in the first vehicle set with the target vehicle; 将所述第一车辆集合中匹配关系损失值最小的车辆确定为与所述目标车辆匹配的所述第一车辆。The vehicle with the smallest matching relationship loss value in the first vehicle set is determined as the first vehicle matching the target vehicle. 6.根据权利要求5所述的方法,其特征在于,所述确定所述第一车辆集合中各个车辆与所述目标车辆的匹配关系损失值,包括:6. The method according to claim 5, characterized in that the determining of the matching relationship loss value between each vehicle in the first vehicle set and the target vehicle comprises: 针对所述第一车辆集合中的每个车辆执行以下操作,在执行以下操作时,所述第一车辆集合中的每个车辆为当前车辆:Perform the following operations for each vehicle in the first vehicle set, where each vehicle in the first vehicle set is a current vehicle: 根据所述当前车辆在所述第二时刻的位置信息和所述目标车辆在第二时刻的预测位置信息,确定第一损失值,其中,所述第一损失值代表所述当前车辆与所述目标车辆之间的质心损失值;Determine a first loss value according to the position information of the current vehicle at the second moment and the predicted position information of the target vehicle at the second moment, wherein the first loss value represents a center of mass loss value between the current vehicle and the target vehicle; 根据所述第二图像中用于标识所述当前车辆的当前目标框与第一目标框,确定第二损失值,其中,所述第一目标框为第一图像中用于标识所述目标车辆的目标框,所述第一图像是通过所述目标拍摄设备在所述第一时刻拍摄得到的图像,所述第一损失值代表所述当前目标框与所述第一目标框之间的重叠面积损失值;Determine a second loss value according to a current target frame and a first target frame in the second image for identifying the current vehicle, wherein the first target frame is a target frame in the first image for identifying the target vehicle, the first image is an image captured by the target capturing device at the first moment, and the first loss value represents an overlapping area loss value between the current target frame and the first target frame; 根据所述当前目标框与所述第一目标框,确定第三损失值,其中,所述第一损失值代表所述当前车辆与所述目标车辆之间的面积相似损失值;Determine a third loss value according to the current target frame and the first target frame, wherein the first loss value represents an area similarity loss value between the current vehicle and the target vehicle; 根据所述第一损失值、所述第二损失值和所述第三损失值确定所述当前车辆与所述目标车辆的匹配关系损失值。A matching relationship loss value between the current vehicle and the target vehicle is determined according to the first loss value, the second loss value and the third loss value. 7.根据权利要求6所述的方法,其特征在于,7. The method according to claim 6, characterized in that 所述根据所述第二图像中用于标识所述当前车辆的当前目标框与第一目标框,确定第二损失值,包括:确定所述当前目标框与所述第一目标框的重叠面积以及所述第一目标框的第一面积;确定所述重叠面积和所述第一面积的比值;将所述比值与1的差值确定为所述第二损失值;Determining the second loss value according to the current target frame and the first target frame used to identify the current vehicle in the second image includes: determining an overlapping area between the current target frame and the first target frame and a first area of the first target frame; determining a ratio of the overlapping area to the first area; and determining a difference between the ratio and 1 as the second loss value; 所述根据所述当前目标框与所述第一目标框,确定第三损失值,包括:确定所述当前目标框的第二面积以及所述第一目标框的第一面积;根据所述第一面积和所述第二面积之间的差值确定为所述第三损失值。Determining the third loss value based on the current target frame and the first target frame includes: determining the second area of the current target frame and the first area of the first target frame; and determining the third loss value based on the difference between the first area and the second area. 8.一种车辆的位置更新装置,其特征在于,包括:8. A vehicle position updating device, comprising: 第一确定模块,用于根据目标车辆在第一时刻的位置信息,确定所述目标车辆在第二时刻的预测位置信息,其中,所述第二时刻晚于所述第一时刻;A first determining module, configured to determine predicted position information of the target vehicle at a second moment according to position information of the target vehicle at a first moment, wherein the second moment is later than the first moment; 获取模块,用于获取在所述第二时刻位于目标范围内的车辆,得到第一车辆集合,其中,所述目标范围是根据所述目标车辆在所述第一时刻的位置信息确定的;An acquisition module, used for acquiring vehicles located within a target range at the second moment to obtain a first vehicle set, wherein the target range is determined according to position information of the target vehicles at the first moment; 第二确定模块,用于根据所述预测位置信息在所述第一车辆集合中确定与所述目标车辆匹配的第一车辆,并将所述第一车辆的位置信息确定为所述目标车辆在所述第二时刻的位置信息。A second determination module is used to determine a first vehicle matching the target vehicle in the first vehicle set according to the predicted position information, and determine the position information of the first vehicle as the position information of the target vehicle at the second moment. 9.一种计算机可读存储介质,其特征在于,所述计算机可读存储介质中存储有计算机程序,其中,所述计算机程序被处理器执行时实现所述权利要求1至7任一项中所述的方法的步骤。9. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, wherein when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 7 are implemented. 10.一种电子装置,包括存储器、处理器以及存储在所述存储器上并可在所述处理器上运行的计算机程序,其特征在于,所述处理器执行所述计算机程序时实现所述权利要求1至7任一项中所述的方法的步骤。10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in any one of claims 1 to 7 when executing the computer program.
CN202211733173.5A 2022-12-30 2022-12-30 Vehicle position updating method and device, storage medium and electronic device Pending CN116127165A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211733173.5A CN116127165A (en) 2022-12-30 2022-12-30 Vehicle position updating method and device, storage medium and electronic device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211733173.5A CN116127165A (en) 2022-12-30 2022-12-30 Vehicle position updating method and device, storage medium and electronic device

Publications (1)

Publication Number Publication Date
CN116127165A true CN116127165A (en) 2023-05-16

Family

ID=86307454

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211733173.5A Pending CN116127165A (en) 2022-12-30 2022-12-30 Vehicle position updating method and device, storage medium and electronic device

Country Status (1)

Country Link
CN (1) CN116127165A (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001229488A (en) * 2000-02-15 2001-08-24 Hitachi Ltd Vehicle tracking method and traffic condition tracking device
CN103927508A (en) * 2013-01-11 2014-07-16 浙江大华技术股份有限公司 Target vehicle tracking method and device
CN111667512A (en) * 2020-05-28 2020-09-15 浙江树人学院(浙江树人大学) Multi-target vehicle track prediction method based on improved Kalman filtering
CN111666860A (en) * 2020-06-01 2020-09-15 浙江省机电设计研究院有限公司 Vehicle track tracking method integrating license plate information and vehicle characteristics
CN212084368U (en) * 2020-06-01 2020-12-04 浙江省机电设计研究院有限公司 A highway vehicle trajectory tracking system
CN112462326A (en) * 2020-11-16 2021-03-09 北邮感知技术研究院(江苏)有限公司 Position information determining method and device, electronic equipment and storage medium

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001229488A (en) * 2000-02-15 2001-08-24 Hitachi Ltd Vehicle tracking method and traffic condition tracking device
CN103927508A (en) * 2013-01-11 2014-07-16 浙江大华技术股份有限公司 Target vehicle tracking method and device
CN111667512A (en) * 2020-05-28 2020-09-15 浙江树人学院(浙江树人大学) Multi-target vehicle track prediction method based on improved Kalman filtering
CN111666860A (en) * 2020-06-01 2020-09-15 浙江省机电设计研究院有限公司 Vehicle track tracking method integrating license plate information and vehicle characteristics
CN212084368U (en) * 2020-06-01 2020-12-04 浙江省机电设计研究院有限公司 A highway vehicle trajectory tracking system
CN112462326A (en) * 2020-11-16 2021-03-09 北邮感知技术研究院(江苏)有限公司 Position information determining method and device, electronic equipment and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
甘玲;潘小雷;: "一种应用于交通环境中的运动车辆跟踪方法", 重庆邮电大学学报(自然科学版), no. 03, 15 June 2013 (2013-06-15) *

Similar Documents

Publication Publication Date Title
CN109883423B (en) Positioning method, system, equipment and storage medium based on Kalman filtering
CN110246182B (en) Vision-based global map positioning method and device, storage medium and equipment
CN113674416B (en) Three-dimensional map construction method and device, electronic equipment and storage medium
US20160178728A1 (en) Indoor Positioning Terminal, Network, System and Method
JP7111175B2 (en) Object recognition system, recognition device, object recognition method, and object recognition program
CN111179311A (en) Multi-target tracking method and device and electronic equipment
US20210097103A1 (en) Method and system for automatically collecting and updating information about point of interest in real space
CN115063454B (en) Multi-target tracking matching method, device, terminal and storage medium
CN113610967B (en) Three-dimensional point detection method, three-dimensional point detection device, electronic equipment and storage medium
CN111507204A (en) Method and device for detecting countdown signal lamp, electronic equipment and storage medium
CN114332232B (en) Smart phone indoor positioning method based on space point, line and surface feature hybrid modeling
KR20150042544A (en) Mobile terminal for providing location information, method and system for measuring the location information
CN116645396A (en) Track determination method, track determination device, computer-readable storage medium and electronic device
CN113063421A (en) Navigation method and related device, mobile terminal and computer readable storage medium
CN112631333B (en) Target tracking method, device and image processing chip for unmanned aerial vehicle
CN115616937A (en) Automatic driving simulation test method, device, equipment and computer readable medium
JP2018536550A (en) Active camera movement determination for object position and range in 3D space
CN113469130B (en) A method, device, storage medium and electronic device for detecting an obstructed target
CN110986916A (en) Indoor positioning method, device, electronic device and storage medium
CN117097989B (en) Image optimization processing method and device
CN115249407A (en) Indicating lamp state identification method and device, electronic equipment, storage medium and product
CN117746418B (en) Target detection model construction method, target detection method and related device
CN113297259A (en) Robot and environment map construction method and device thereof
CN116127165A (en) Vehicle position updating method and device, storage medium and electronic device
CN118015559A (en) Object identification method and device, electronic equipment and storage medium

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