Performance evaluation of low-resolution monocular vision-based velocity estimation technique for moving obstacle detection and tracking
by R. Rajesh; P.V. Manivannan
International Journal of Vehicle Autonomous Systems (IJVAS), Vol. 17, No. 1/2, 2023

Abstract: Moving obstacle detection and its velocity estimation is crucial in an autonomous vehicle for better manoeuvring in real-road conditions. A modified spatial calibration approach proposed in this paper leverages the monocular vision system to estimate the velocity of detected moving obstacle by solving the Scaling Factor (SF) problem. Further, the Least Absolute Residual (LAR)/bisquare method-based SF smoothing is proposed to estimate the velocity much more accurately in day and night conditions. Moreover, the effect of camera resolution in velocity estimation has been studied by testing the developed moving obstacle detection and velocity estimation algorithm with data obtained from a Low-Resolution (LR) and High-Resolution (HR) monocular vision camera and also compared with the estimated velocity from the stereo vision system. The comparison concludes that using the proposed approach, the LR monocular vision provides a cost-effective, computationally inexpensive solution with reduced error and better detection range.

Online publication date: Fri, 19-Jan-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Autonomous Systems (IJVAS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com