A Shadow Elimination Method for Vehicle Analysis Based On Random Walk
A novel method is proposed for solving the shadow and occlusion problems of vehicle analysis. Kalman filter is combined with random walk algorithm. First, the computation region of random walk is reduced through the prediction information from the Kalman filter, then the seed points is extracted in this region for segmentation. Further, the segmentation of random walk is implemented, and the results of which is used to update the filter parameters. In order to obtain the initial state vector for Kalman filter, the random walk based on car bottom shadow is proposed too. Experiment results show that the problem of moving vehicles shadows, tracking and occlusion can be solved.
Random Walk Kalman Filter Mark Point Tracking and Traffic
Liu Meng Wu Chengdong Wang Li Ji Peng
School of Information Science and Engineering, Northeastern University, Shenyang 110004
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
2099-2103
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）