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

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.