CAMShift tracking algorithm for metro entrance and exit security

摘要

In view of security issues, this paper studies how to track suspicious human at the metro entrance and exit accurately and quickly. A novel tracking method combining Kalman filter and SURF algorithm embedded in CAMShift target tracking algorithm was proposed. Firstly, the algorithm chooses target template for tracking by hand, then the Bhattacharyya coefficient is calculated by comparing the comprehensive histogram of chromaticity and gradient direction between the candidate target and the template target. If the Bhattacharyya coefficient between the template with the candidate target area is greater than the given threshold for detection, the tracking process will be failed. At this time, the SURF algorithm is used for feature matching on tracking result window in current search window and previous frame, and the size and position of the suspicious person are recalculated. At the same time, in order to avoid the failure of suspicious personnel tracking caused by occlusion, Kalman filter is used to predict and update the moving window so as to determine the central position of the search window in the next frame. Experiments show that the proposed algorithm can stably track the suspicious target personnel even when adverse effects such as color disturbance, partial occlusion, and complex image background occur.