摘要
针对被跟踪目标发生严重遮挡、暂时离开跟踪画面或位移发生重大变化时,采用基于在线Boosting跟踪的邻近区间更新算法导致错误累积进而产生漂移甚至跟踪失败的问题,提出一种改进的基于在线Boosting的目标跟踪方法。由在线Boosting算法对分类器特征库进行更新,使用卡尔曼滤波动态更新阈值,使系统能根据跟踪目标的置信度自动用获取到的局部特征对阈值做相应调整。当运动目标的置信度低于下限阈值时,采用Blob跟踪方法,根据颜色和空间上的相似性将目标分割为多个区域,每个区域包含有区域号、位置、大小信息,随机选取一个进入在线Boosting跟踪模块进行检测,直到获取到置信度达到上限阈值时,切换到邻近区域更新算法进行跟踪。对不同视频序列测试的结果表明,与传统在线Boosting算法和其他跟踪算法相比,所提出算法能快速准确获取跟踪目标并具有更强的鲁棒性。
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.
出处
《计算机应用》
CSCD
北大核心
2013年第2期495-498,502,共5页
journal of Computer Applications
基金
教育部博士点基金资助项目(20113227110010)
江苏省高校自然基金资助项目(10KJB520004)
江苏省软件与集成电路专项基金资助项目(2009[100])
