Title: Vehicle rear end collision warning method based on MeanShift and Kalman filter tracking

Authors: Bin Fang

Addresses: Department of Traffic Administration and Engineering, Hunan Police Academy, Changsha, 410138, China

Abstract: To reduce the false alarm rate and alarm rate of vehicle rear end warning, and improve the accuracy of warning, a vehicle rear end warning method based on MeanShift and Kalman filter tracking is proposed. Determine the rear end warning area, use a high-speed camera to capture the driving video image of the target vehicle, and perform image denoising and enhancement; detect the target vehicle through inter frame difference method, and combine the MeanShift algorithm with the Kalman filter algorithm to complete the tracking and positioning of the target vehicle; Determine the position of the target vehicle and the relationship between the warning areas to achieve rear end warning. The experimental results show that the method proposed in this paper can effectively reduce the false alarm rate and alarm rate, with a false alarm rate always below 2% and an alarm rate of 98%. It has good application performance.

Keywords: rear end collision of vehicles; collision warning; MeanShift algorithm; Kalman filtering algorithm; warning area.

DOI: 10.1504/IJVD.2024.137838

International Journal of Vehicle Design, 2024 Vol.95 No.1/2, pp.1 - 21

Received: 25 Apr 2023
Accepted: 05 Sep 2023
Published online: 05 Apr 2024 *

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