Vehicle Speed Measurement Using Stereo Camera Pair

Najman, Pavel; Zemčík, Pavel

doi:10.1109/tits.2020.3035262

Cited by 8 publications

(16 citation statements)

References 22 publications

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“…Many efforts have been made to acquire environment data without the use of lidars and radars. The methods in [6,7] used stereo cameras and proper calibration to estimate the distance of a target object. Using a monocular (single) camera, approaches based on optical flow have been used to estimate the velocity of a target object [8].…”

Section: Introductionmentioning

confidence: 99%

A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars

2021

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Perception in terms of object detection, classification, and dynamic estimation (position and velocity) are fundamental functionalities that autonomous agents (unmanned ground vehicles, unmanned aerial vehicles, or robots) have to navigate safely and autonomously. To date, various sensors have been used individually or in combination to achieve this goal. In this paper, we present a novel method for leveraging millimeter wave radar’s (mmW radar’s) ability to accurately measure position and velocity in order to improve and optimize velocity estimation using a monocular camera (using optical flow) and machine learning techniques. The proposed method eliminates ambiguity in optical flow velocity estimation when the object of interest is at the edge of the frame or far away from the camera without requiring camera–radar calibration. Moreover, algorithms of various complexity were implemented using custom dataset, and each of them successfully detected the object and estimated its velocity accurately and independently of the object’s distance and location in frame. Here, we present a complete implementation of camera–mmW radar late feature fusion to improve the camera’s velocity estimation performance. It includes setup design, data acquisition, dataset development, and finally, implementing a lightweight ML model that successfully maps the mmW radar features to the camera, allowing it to perceive and estimate the dynamics of a target object without any calibration.

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Section: Introductionmentioning

confidence: 99%

A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars

2021

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“…However, these devices are easy to be detected due to the transmitted signals, which is undesirable for secret measurement. Video-based speed measurement has gained more and more attention because of its low cost, easy concealment and convenient combination of vehicle speed and vehicle information [ 11 , 12 , 13 , 14 , 15 ]. According to the video acquisition way, video-based methods can be further divided into two main categories: two-dimensional (2D) video-based method and three-dimensional (3D) video-based method.…”

Section: Introductionmentioning

confidence: 99%

“…The methods in [ 13 , 14 , 15 ] belong to 3D video-based speed measurement method. A vehicle speed measurement method based on traditional object detection with image processing is proposed in [ 13 ], in which the vehicle target is detected by background subtraction.…”

Section: Introductionmentioning

confidence: 99%

“…Speeded-up robust features (SURF) matching is performed on the vehicle target detected in the left and right view images, and the vehicle speed is estimated with the depth map. A vehicle plate speed measurement method based on WaldBoost classifier object detection is proposed in [ 14 ]. The vehicle plate is detected according to the local binary pattern (LBP) feature, stereo matching and 3D ranging are performed, hence the vehicle speed is calculated.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Stereo Matching Algorithm for Vehicle Speed Measurement System Based on Spatial and Temporal Image Fusion

Yang

Song

et al. 2021

Entropy

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This paper proposes an improved stereo matching algorithm for vehicle speed measurement system based on spatial and temporal image fusion (STIF). Firstly, the matching point pairs in the license plate area with obviously abnormal distance to the camera are roughly removed according to the characteristic of license plate specification. Secondly, more mismatching point pairs are finely removed according to local neighborhood consistency constraint (LNCC). Thirdly, the optimum speed measurement point pairs are selected for successive stereo frame pairs by STIF of binocular stereo video, so that the 3D points corresponding to the matching point pairs for speed measurement in the successive stereo frame pairs are in the same position on the real vehicle, which can significantly improve the vehicle speed measurement accuracy. LNCC and STIF can be used not only for license plate, but also for vehicle logo, light, mirror etc. Experimental results demonstrate that the vehicle speed measurement system with the proposed LNCC+STIF stereo matching algorithm can significantly outperform the state-of-the-art system in accuracy.

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“…The existing velocity measurement methods include Lidar, Radar, and video/image. Video/image measurement methods are being developed because they have the potential to measure speed and identify vehicle numbers [12], [13]. Moreover, video/image speed measurements can be used for autonomous car control systems [14].…”

Section: Introductionmentioning

confidence: 99%

3D Velocity Measurement of Translational Motion using a Stereo Camera

Ristanto¹,

Nugroho²,

Sulistya³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

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This research aims to create a 3D velocity measurement system using a stereo camera. The 3D velocity in this paper is the velocity which consists of three velocity components in 3D Cartesian coordinates. Particular attention is focused on translational motion. The system set consists of a stereo camera and a mini-PC with Python 3.7, and OpenCV 4.0 installed. The measurement method begins with the selection of the measured object, object detection using template matching, disparity calculation using the triangulation principle, velocity calculation based on object displacement information and time between frames, and the storage of measurement results. The measurement system's performance was tested by experimenting with measuring conveyor velocity from forward-looking and angle-looking directions. The experimental results show that the 3D trajectory of the object can be displayed, the velocity of each component and the speed as the magnitude of the velocity can be obtained, and so the 3D velocity measurement can be performed. The camera can be positioned forward-looking or at a certain angle without affecting the measurement results. The measurement of the speed of the conveyor is 11.6 cm/s with an accuracy of 0.4 cm/s. The results of this study can be applied in the performance inspection process of conveyors and other industrial equipment that requires speed measurement. In addition, it can also be developed for accident analysis in transportation systems and practical tools for physics learning.

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Vehicle Speed Measurement Using Stereo Camera Pair

Cited by 8 publications

References 22 publications

A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars

A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars

An Improved Stereo Matching Algorithm for Vehicle Speed Measurement System Based on Spatial and Temporal Image Fusion

3D Velocity Measurement of Translational Motion using a Stereo Camera

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