Method for Shadow and Occlusion Based on the Improved Random Walk Algorithm

An image segmentation algorithm which combines Kalman filter with random walk algorithm is proposed for resolving the problem of shadow and occlusion in traffic video monitoring.Firstly, the predicted information of Kalman filter is used to reduce the working region of the random walk, in which region several mask points are extracted for the segmentation of the shadow and occluded objects.Secondly, the segmentation results of the random walk provide accurate observation information to update the parameters of the Kalman filter.At the same time, a random walk algorithm based on car bottom shadow is proposed to perform initial target segmentation, which is used to obtain the initial state vector of the Kalman filter.Experimental results show that the proposed algorithm can solve the shadow and occlusion problem.And the average accuracy rate of moving vehicle segmentation is more than 94%.Furthermore, the proposed algorithm has real-time performance.