Adaptive Appearance Tracking Model Using Subspace Learning Method

摘要：

Visual tracking is still a challenging subject due to the targeted objects change in direction and size, stochastic disturbance under complicated scene. In the work, we proposed a visual tracking framework based on the subspace updating and learning. We introduced the Halls subspace updating algorithm and the new measurement on subspaces similarity in computing particles weights under Condensation algorithm in our tracking processes. Differed from conventional PCA method, our method adaptively updated the subspace which can reflect appearance variation of the moving target over long period of time. Compared with Condensation algorithm using color histogram, the tracker we proposed can effectively track the target under complicated surrounding.

关键词： Object tracking Subspace learning Adaptive Subspace distance

作者: Gang Wu Zhenmin Tang

作者单位: Department of Vehicle Engineering Nanjing Institute of Technology Nanjing, China School of Computer Science & Technology Nanjing University of Science and Technology Nanjing, China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 413-416

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）