WO2021237750A1 - Method and apparatus for vehicle length estimation - Google Patents

Method and apparatus for vehicle length estimation Download PDF

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Publication number
WO2021237750A1
WO2021237750A1 PCT/CN2020/093539 CN2020093539W WO2021237750A1 WO 2021237750 A1 WO2021237750 A1 WO 2021237750A1 CN 2020093539 W CN2020093539 W CN 2020093539W WO 2021237750 A1 WO2021237750 A1 WO 2021237750A1
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WO
WIPO (PCT)
Prior art keywords
vehicle
length
roadside
point
curve function
Prior art date
Application number
PCT/CN2020/093539
Other languages
English (en)
French (fr)
Inventor
Jie Zhao
Jie MIN
Xiaodong Xu
Original Assignee
Siemens Ltd., China
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 Siemens Ltd., China filed Critical Siemens Ltd., China
Priority to PCT/CN2020/093539 priority Critical patent/WO2021237750A1/en
Priority to CN202080101050.2A priority patent/CN115668297A/zh
Priority to EP20937268.9A priority patent/EP4139891A4/de
Publication of WO2021237750A1 publication Critical patent/WO2021237750A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior

Definitions

  • the present invention relates to techniques of image perception, and more particularly to a method, apparatus and computer-readable storage medium for vehicle length estimation.
  • Image perception technology for intelligent traffic system is an important research direction, with which vehicle’s length, speed, etc. can be calculated, for regulation of driving or parking vehicles.
  • Image perception technology usually bases on images captured by cameras. For city, most of monitoring cameras are dome cameras (as shown in FIG. 1) , as they can provide a wide range of view. However, dome cameras can also cause serious distortion in image. For example, the straight line seems to be bent in the image as shown in FIG. 2. In this case, it is tough to calculate the accurate distance from the image.
  • the calibration is a necessary step.
  • a trapezoid is calibrated on the image, and each side of the trapezoid is measured in real word. In this way, the distance can be calculated roughly, as shown in FIG. 3.
  • this method is applied to estimate the speed of the vehicle roughly.
  • it is assumed that there is no distortion in the image with this method, and it can only provide a rough estimation. This method will cause serious error if there is obvious distortion in image.
  • Embodiments of the present disclosure include methods, apparatuses for object recognition and methods, apparatuses for vehicle length estimation.
  • a method for vehicle length estimation includes following steps:
  • d is the real distance from a specific point to a reference point at the roadside
  • x and y are pixel coordinates of the specific point on the image
  • the curve function is pre-calculated based on at least two preset calibration points including the reference point along the roadside.
  • an apparatus for vehicle length estimation includes:
  • an image acquisition module configured to acquire an image from a camera which monitors a roadside
  • a vehicle detection module configure to obtain a bounding box of a vehicle via object recognition
  • an apparatus for vehicle length estimation includes at least one processor; at least one memory, coupled to the at least one processor, configured to execute method according to the first aspect.
  • a computer-readable medium for vehicle length estimation stores computer-executable instructions, wherein the computer-executable instructions when executed cause at least one processor to execute method according to the first aspect.
  • FIG. 1 depicts a dome camera.
  • FIG. 2 depicts serious distortion in dome camera.
  • FIG. 3 depicts trapezoid calibration.
  • FIG. 4 depicts a block diagram of an apparatus for vehicle length estimation in accordance with one embodiment of the present disclosure.
  • FIG. 5 depicts calibration methodology deployed in accordance with one embodiment of the present disclosure.
  • FIG. 6 depicts curve regression in accordance with one embodiment of the present disclosure.
  • FIG. 7 depicts vehicle length estimation for top view in accordance with one embodiment of the present disclosure.
  • FIG. 8 depicts vehicle length estimation for side view in accordance with one embodiment of the present disclosure.
  • FIG. 9 depicts a flow diagram of a method for vehicle length estimation in accordance with one embodiment of the present disclosure.
  • FIG. 10 depicts an example procedure in accordance with one embodiment of the present disclosure.
  • FIG. 11 depicts the vehicle length estimation result in accordance with one embodiment of the present disclosure.
  • the articles “a” , “an” , “the” and “said” are intended to mean that there are one or more of the elements.
  • the terms “comprising” , “including” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements.
  • Vehicle length estimation solutions are proposed in this disclosure, which can be used to calculate the vehicle length at roadside parking space.
  • the first step is calibration which can be based on curving fitting. After calibration, the curve function can be calculated.
  • the vehicle bounding box can be obtained with object detection technology.
  • the vehicle length can be estimated according to the camera views: top view and side view. Now the present disclosure will be described hereinafter in details by referring to FIG. 4 to FIG. 11.
  • a ruler can be placed along the roadside, beside these calibration points, to mark real distance d i of a specific calibration point P i from the reference point P 0 .
  • These reference points can be deployed evenly along the roadside, or otherwise, as long as there is a ruler which can measure real distance of a specific calibration point from the reference point.
  • d i is the real distance from reference point P 0 to P i in meter
  • h i is the pixel distance, which can be imagined as the pole with fixed length standing on the calibration point.
  • the x i and y i are the pixel coordinates in image coordinate system which means it is located in the x i th column and y i th row.
  • h i can be assigned, for example, 22 pixels, corresponding to 1.5m in the real distance.
  • the parking space extends in y direction which means the coordinate y i contributes much more than x i .
  • d and y can be obtained directly from the calibration point.
  • the regression curve can be calculated as FIG. 6.
  • the coefficients calculated out for the example shown in FIG. 6 are:
  • the parking space extends in x direction which means the coordinate x i contributes much more than y i .
  • the curve function can be written as:
  • top view As shown in FIG. 7, and side view, as shown in FIG. 8.
  • the height of the bounding box contains both height and length component of the vehicle.
  • h changes with d the larger is d, the smaller is h.
  • the functional relationship between h and d is not linear and is also influenced by distortion introduced by dome camera. So here, With the same curve fitting method, the function between h and d can be obtained as:
  • dome camera is only an example for cameras with distortion on images.
  • the solutions provided in the present disclosure can also be applicable to any kind of cameras with image distortion problems.
  • the vehicle heights can be different.
  • the height of a bus can be about 2.5m, therefore the pixel height of bus in image is
  • h type takes into vehicle type, which can be calculated out based on a car’s common value, that is 1.5 meters.
  • the length of vehicle L can be calculated out, eliminating distortion brought in by dome camera and influence of height by the top view.
  • the curve function for side view can be written as formula (2) .
  • two regression lines are needed, the outer line f 1 (x) and the inner line f 2 (x) , which are two lines in parallel with the roadside, one passes through the start point of the vehicle and the other passes through the end point of the vehicle. If the vehicle bounding box is (x i , y i , w i , h i ) , then the starting position is the crossing point P 1 , and the end position is P 2 . Therefore, the vehicle length can be calculated as:
  • FIG. 4 depicts a block diagrams of an apparatus in accordance with one embodiment of the present disclosure.
  • the apparatus 10 for vehicle length estimation presented in the present disclosure can be implemented as a network of computer processors, to execute following method 100 for vehicle length estimation presented in the present disclosure.
  • the apparatus 10 can also be a single computer, as shown in FIG. 4, including at least one memory 101, which includes computer-readable medium, such as a random access memory (RAM) .
  • the apparatus 10 also includes at least one processor 102, coupled with the at least one memory 101.
  • Computer-executable instructions are stored in the at least one memory 101, and when executed by the at least one processor 102, can cause the at least one processor 102 to perform the steps described herein.
  • the at least one processor 102 may include a microprocessor, an application specific integrated circuit (ASIC) , a digital signal processor (DSP) , a central processing unit (CPU) , a graphics processing unit (GPU) , state machines, etc.
  • embodiments of computer-readable medium include, but not limited to a floppy disk, CD-ROM, magnetic disk, memory chip, ROM, RAM, an ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other medium from which a computer processor can read instructions.
  • various other forms of computer-readable medium may transmit or carry instructions to a computer, including a router, private or public network, or other transmission device or channel, both wired and wireless.
  • the instructions may include code from any computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, and JavaScript.
  • the at least one memory 101 shown in FIG. 4 can contain a vehicle length estimation program 20, when executed by the at least one processor 102, causing the at least one processor 102 to execute the method 100 for vehicle length estimation presented in the present disclosure.
  • Images 30 of vehicles can also be stored in the at least one memory 101. These data can be received via a communication module 103 of the apparatus 10.
  • a dome camera 40 can be connected to the apparatus 10 via the communication module 102. The dome camera 40 can take images of the roadside it monitors and send taken images to the apparatus 10 for further processing.
  • the calibration process can also be executed on the apparatus 10, which depends on device configuration and processing competence.
  • the calibration program can be part of the vehicle length estimation program 20 and can be pre-stored in the at least memory 101.
  • the vehicle length estimation program 20 can include:
  • an image acquisition module 201 configured to acquire an image 30 from a camera 40 which monitors a roadside;
  • a vehicle detection module 202 configure to obtain a bounding box of a vehicle via object recognition
  • the image acquisition module 201, the vehicle detection module 202, and the estimation module 203 are described above as software modules of the vehicle length estimation program 20. Also, they can be implemented via hardware, such as ASIC chips. They can be integrated into one chip, or separately implemented and electrically connected.
  • FIG. 4 The architecture above is merely exemplary and used to explain the exemplary method 100 shown in FIG. 9.
  • One exemplary method 100 according to the present disclosure includes following steps:
  • S101 acquiring an image 30 from a camera 40 which monitors a roadside;
  • FIG. 10 and FIG. 11 an example for process of calibration and vehicle length estimation, and corresponding application and result will be described.
  • the vehicle’s bounding box can be obtained with object detection, and with the input of bounding box and vehicle type, the vehicle length can be estimated according to the camera view, i.e., top view and side view.
  • the performance and result can be seen in FIG. 11.
  • the location of the target vehicle can be seen on the map (referring to the left second picture on top) , also detailed information of the vehicle and its parking action can be seen on the left first picture on top, the vehicle length is 5 meter in this case.
  • On bottom are four pictures depicting the car’s parking action.
  • On the right picture each point corresponds to a parking lot, and different colors indicate different parking statuses.
  • a computer-readable medium is also provided in the present disclosure, storing computer-executable instructions, which upon execution by a computer, enables the computer to execute any of the methods presented in this disclosure.
  • a computer program which is being executed by at least one processor and performs any of the methods presented in this disclosure.
  • the proposed novel calibration solution can work well in dome cameras which have serious distortion, and the vehicle length estimation solution can work well for both top view and side view cases, which can reduce the error caused by vehicle height in top view.
PCT/CN2020/093539 2020-05-29 2020-05-29 Method and apparatus for vehicle length estimation WO2021237750A1 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
PCT/CN2020/093539 WO2021237750A1 (en) 2020-05-29 2020-05-29 Method and apparatus for vehicle length estimation
CN202080101050.2A CN115668297A (zh) 2020-05-29 2020-05-29 用于车辆长度估计的方法和设备
EP20937268.9A EP4139891A4 (de) 2020-05-29 2020-05-29 Verfahren und vorrichtung zur schätzung der fahrzeuglänge

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Application Number Priority Date Filing Date Title
PCT/CN2020/093539 WO2021237750A1 (en) 2020-05-29 2020-05-29 Method and apparatus for vehicle length estimation

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CN108898840A (zh) * 2018-05-08 2018-11-27 江苏理工学院 一种基于视频监控的智能交通信号灯控制方法
US20180365858A1 (en) * 2017-06-14 2018-12-20 Hyundai Mobis Co., Ltd. Calibration method and apparatus
CN109255316A (zh) * 2018-08-30 2019-01-22 深圳市路畅科技股份有限公司 一种车道偏移检测方法及系统
CN110307791A (zh) * 2019-06-13 2019-10-08 东南大学 基于三维车辆边界框的车辆长度及速度计算方法

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CN107248178A (zh) * 2017-06-08 2017-10-13 上海赫千电子科技有限公司 一种基于畸变参数的鱼眼相机标定方法
US20180365858A1 (en) * 2017-06-14 2018-12-20 Hyundai Mobis Co., Ltd. Calibration method and apparatus
CN108898840A (zh) * 2018-05-08 2018-11-27 江苏理工学院 一种基于视频监控的智能交通信号灯控制方法
CN109255316A (zh) * 2018-08-30 2019-01-22 深圳市路畅科技股份有限公司 一种车道偏移检测方法及系统
CN110307791A (zh) * 2019-06-13 2019-10-08 东南大学 基于三维车辆边界框的车辆长度及速度计算方法

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EP4139891A4 (de) 2024-02-14
EP4139891A1 (de) 2023-03-01

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