关键词: 在线Boosting, 目标跟踪, 漂移, 抗遮挡, 运动检测

Abstract: When the tracked targets get seriously obscured, temporarily leave the tracking screen or have significant displacement variation, adjoining interval updating algorithm based on on-line Boosting will lead to the error accumulation thus producing the drift or even tracking failure. Therefore, a reformative target tracking method based on on-line Boosting was proposed. The classifier feature library was updated by using on-line Boosting algorithm, and the threshold was dynamically renewed by using Kalman filter, hence the system could automatically capture the local features and apply corresponding adjustment to the value of threshold according to the tracking confidence of the object. When the confidence of the moving target was less than the lower threshold value, Blob tracking methodology would be applied. It processed as follows: the target was segmented into many regions according to the similarity of both color and space, and each single region contained the information of region number, location and size. One of the regions would be randomly selected into an on-line Boosting tracking module for testing, and the switch to the adjacent region by applying update algorithm for tracking would not happen unless the captured confidence level reached the upper threshold. Results of tests on different video sequences show that the proposed algorithm is capable of speedily and accurately capturing the target object real-time and holding a better robustness in comparison of the traditional on-line Boosting algorithm and other tracking algorithms.

Key words: on-line Boosting, object tracking, drifting, anti-occlusion, moving detection