Single–camera vehicle speed measurement using the geometry of the imaging system

Vakili, Elnaz; Shoaran, Maryam; Sarmadi, M.

doi:10.1007/s11042-020-08761-5

Cited by 20 publications

(28 citation statements)

References 24 publications

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“…Second, those methods focused on tracking the centroid of the blob or the bounding box of the vehicle [25, 34, 35, 40, 48, 50, 52, 56, 65, 72, 73, 75, 83, 86, 95, 103, 106, 111, 118, 121, 122]. Finally, those approaches that focus on tracking the entire vehicle [15, 16, 18, 26–30, 32, 36, 42–44, 47, 54, 57, 63, 78, 81, 84, 89, 91, 98, 102, 104, 105, 108, 109, 111, 113, 114, 120, 125, 128, 129], or a specific part of it (such as the license plate [41, 49, 60, 66, 68, 70, 76, 77, 80, 82, 94, 100, 125]).…”

Section: Taxonomymentioning

confidence: 99%

Vision‐based vehicle speed estimation: A survey

Llorca

Martínez

Daza

2021

IET Intelligent Trans Sys

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The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits are considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision‐based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision‐based vehicle speed estimation. The terminology and the application domains are described and a complete taxonomy of a large selection of works that categorizes all stages involved is proposed. An overview of performance evaluation metrics and available datasets is provided. Finally, current limitations and future directions are discussed.

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Section: Taxonomymentioning

confidence: 99%

Vision‐based vehicle speed estimation: A survey

Llorca

Martínez

Daza

2021

IET Intelligent Trans Sys

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“…Another way is to determine the geometry of the environment and use it to understand scene. For, example, [13] used the known height and angle of the camera to understand the environment and calculated speed using the distance traveled by a car. They detected the license plate's corner points to track the car, which adds another limitation, as cars can only be tracked with the license plate in view.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed method does not require camera-radar calibration as in [13,14], making the setup ad hoc, simple, reliable, and adaptive.…”

mentioning

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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“…Lu et al [25] proposed a method that used difference images to detect vehicle movement and distance. Other motion detection methods [26], [27] have been applied to estimate vehicle speeds from fixed locations. However, fixed-location cameras and CCTVs have problems with expanded areas, and they often have difficulty with lines and background features.…”

Section: A Related Workmentioning

confidence: 99%

Vehicle Speed Measurement Methodology Robust to Playback Speed-Manipulated Video File

Shim¹,

Park

Kim

et al. 2021

IEEE Access

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Concomitant with the virtual ubiquity of dashcams in vehicles, the instantaneous speed of vehicles during accidents can now be measured using dashcam videos to understand the causes of such accidents. In this study, we estimated vehicle speeds using dashcam videos recorded in various road environments and analyzed them by comparing them to ground truth. We also considered various scenarios yielding retaken and re-encoded videos from additional recording devices, depending on the circumstances, wherein the original video can be manipulated in terms of playback speed and/or frames per second. The experimental results revealed that the actual and measured speeds were similar for most user-specific intervals. Moreover, the results suggest that our estimated speed is adequately valid as evidence, regardless of retake and re-encoding states. Furthermore, we adopted a statistical approach (i.e., Pearson's correlation coefficient analysis) to verify the proposed vehicle speed estimation method. The results showed that when playback speed was manipulated in re-encoded or retaken video, the proposed method consistently measured speeds at 99.99 % accuracy. This verified method will play an important role in deriving reliable results from various evidential video sources. INDEX TERMS dashcam, retake •re-encoded video, vehicle speed measurement, video forensic, video manipulation

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Single–camera vehicle speed measurement using the geometry of the imaging system

Cited by 20 publications

References 24 publications

Vision‐based vehicle speed estimation: A survey

Vision‐based vehicle speed estimation: A survey

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

Vehicle Speed Measurement Methodology Robust to Playback Speed-Manipulated Video File

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