Accurate distance estimation using camera orientation compensation technique for vehicle driver assistance system

Cheung, Hoi-Kok; Siu, Wan-Chi; Lee, Steven; Poon, L. C. K.; Ng, Chiu-Shing

doi:10.1109/icce.2012.6161840

Cited by 16 publications

(4 citation statements)

References 4 publications

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“…Focal length and disparity image were used to determine the real distance of the detected vehicles. Lane detection and pin‐hole camera model were used and fused together in [26] to improve the robustness of distance estimation. Moreover, camera orientation compensation technique was employed for accurate distance estimation in work [27].…”

Section: Related Workmentioning

confidence: 99%

Joint vehicle detection and distance prediction via monocular depth estimation

Shen

Zhao

Liu

et al. 2020

IET Intelligent Transport Systems

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Section: Related Workmentioning

confidence: 99%

Joint vehicle detection and distance prediction via monocular depth estimation

Shen

Zhao

Liu

et al. 2020

IET Intelligent Transport Systems

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“…However, this method also does not consider lateral distance and the influence of camera attitude angles. Cheung et al 7 improved the pinhole model by detecting the vanishing point by lane lines to correct the pitch angle of the camera. However, the yaw angle of the camera is still not considered.…”

Section: Introductionmentioning

confidence: 99%

Inter-vehicle distance estimation considering camera attitude angles based on monocular vision

Liu

Zhang

Hou

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Perceiving the distance between vehicles is a crucial issue for advanced driving assistance systems. However, most vision-based distance estimation methods do not consider the influence of the change in camera attitude angles during driving or only use the vanishing point detected by lane lines to correct the pitch angle. This paper proposed an improved pinhole distance estimation model based on the road vanishing point without the lane line information. First, the road vanishing point is detected based on the dominant texture orientation, and the yaw and pitch angles of the camera are estimated. Then, a distance estimation model considering attitude angle compensation is established. Finally, the experimental results show that the proposed method can effectively correct the influence of the camera attitude angle on the distance estimation results.

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“…The works are done by [8] estimated distance by means of using geometric relation yielded a quite good result of the accuracy of 90.29 % with claimed error average 9.71 %. The research done by [9] utilized a homography matrix and Rodrigues Identity to compute the rotation matrix, the distance between vehicle calculated from this rotation matrix, however, it used a different method to detect the vehicles with the one proposed in this research.…”

Section: Introductionmentioning

confidence: 99%

Vehicular networking and computer vision-based distance estimation for VANET application using Raspberry Pi 3

Pratama

Gruosso

Santoso

et al. 2019

J. Mechatron. Electr. Power Veh. Technol.

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This research was implementing vehicle networking using WIFI connection and computer vision to measure the distance of vehicles in front of a driver. In particular, this works aimed to improve a safe driving environment thus supporting the current technology concept being developed for inter-vehicular networking, VANET, especially in its safety application such as Overtaking Assistance System. Moreover, it can wirelessly share useful visual information such as hazard area of a road accident. In accordance with Vehicle-to-Vehicle (V2V) concept, a vehicle required to be able to conduct networking via a wireless connection. Useful data and video were the objects to be sent over the network established. The distance of a vehicle to other vehicles towards it is measured and sent via WIFI together with a video stream of the scenery experienced by the front vehicle. Haar Cascade Classifier is chosen to perform the detection. For distance estimation, at least three methods have been compared in this research and found evidence that, for measuring 5 meters, the iterative methods shows 5.80. This method performs well up to 15 meters. For measuring 20 meters, P3P method shows a better result with only 0.71 meters to the ground truth. To provide a physical implementation for both the detection and distance estimation mechanism, those methods were applied in a compact small-sized vehicle-friendly computer device the Raspberry Pi. The performance of the built system then analyzed in terms of streaming latency and accuracy of distance estimation and shows a good result in measuring distance up to 20 meters.

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Accurate distance estimation using camera orientation compensation technique for vehicle driver assistance system

Cited by 16 publications

References 4 publications

Joint vehicle detection and distance prediction via monocular depth estimation

Joint vehicle detection and distance prediction via monocular depth estimation

Inter-vehicle distance estimation considering camera attitude angles based on monocular vision

Vehicular networking and computer vision-based distance estimation for VANET application using Raspberry Pi 3

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