在线特征选择的目标跟踪  

Online select feature target tracking

作　　者：杨恢先[1] 杨心力[1] 曾金芳[1] 于洪[2] 

机构地区：[1]湘潭大学材料与光电物理学院,湖南湘潭411105 [2]琼州学院物理系,海南五指山572200

出　　处：《计算机应用研究》2010年第3期1180-1182,共3页Application Research of Computers

基　　金：海南省自然科学基金资助项目(60897);海南省教育厅资助项目(Hj2009-135)

摘　　要：为提高目标与背景对比度低、相似物体干扰等复杂环境下目标跟踪的效果,提出将在线学习选择最优颜色特征嵌入跟踪算法中,以改善跟踪的稳定性。以当前时刻目标的区域为目标区域,利用卡尔曼滤波预测目标的下一时刻位置,在卡尔曼滤波预测的位置为中心取某一区域作为背景区域进行在线特征选择作为下一时刻的跟踪特征,以卡尔曼滤波预测的位置为初始位置利用Mean-shift搜索目标位置,此位置作为量测进行卡尔曼滤波校正。通过实验表明,该方法在目标与背景的对比度低、相似物体干扰等复杂环境下极大地改善了跟踪的稳定性。In order to improve robustness of tracking system under complex surrounding such as low-contrast, similar to object interference, this paper proposed that online select optimal color feature mechanism was embedded in target tracking algorithm. The target region of current moment was seen as the target region, employed Kalman filter predict the target position in next moment, and selected a region in the predicted position as the background region. Employ this two region online learning and select the optimal color feature as tracking feature of the next moment. The Kalman filter predicted position as initial position, utilized Mean-shift search the target position. Used this searched position as measurement correct the Kalman filter. Experimental results show that this method can greatly improve the robustness of target tracking method under complex surrounding such as low-contrast, similar to object interference.

关 键 词：在线学习 最优颜色特征 均值向量平移 卡尔曼滤波 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]