西安电子科技大学学报（自然科学版）2016，Vol.43Issue(5)：98-104,7.DOI:10.3969/j.issn.1001-2400.2016.05.018
在线低秩表示的目标跟踪算法
Object tracking via online low rank representation
摘要
Abstract
Object tracking is an active research topic in computer vision . The traditional tracking methods based on the generative model are sensitive to noise and occlusion , which leads to the failure of tracking results . In order to solve this problem , the tracking results of the first few frames are used as the observation matrix , and the low rank features of the observation model are solved by the the RPCA model . When the new video streams come , a new incremental RPCA is proposed to compute the new observation matrix by the augmented Lagrangian algorithm . The tracking model is established in the Bayesian framework , and the dictionary matrix is updated with the low rank feature . We have tested the proposed algorithm and six state‐of‐the‐art approaches on eight publicly available sequences . Experimental results show that the proposed method has a lower pixel center position error and a higher overlap ratio .关键词
目标跟踪/低秩特征/鲁棒的主成分分析模型/字典矩阵Key words
object tracking/low rank feature/RPCA model/dictionary matrix分类
信息技术与安全科学引用本文复制引用
王海军,葛红娟,张圣燕..在线低秩表示的目标跟踪算法[J].西安电子科技大学学报（自然科学版）,2016,43(5):98-104,7.基金项目
山东省自然科学基金资助项目（ZR2015FL009）；滨州市科技发展计划资助项目（2013ZC0103）；滨州学院科研基金资助项目 （）
