Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

作     者：Deng, Ting-quan Dai, Jia-shu Dong, Tian-zhen Yi, Ke-jia 

作者机构：Harbin Engn Univ Coll Sci Harbin 150001 Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin 151001 Peoples R China Syst Engn Res Inst CSSC Sci & Technol Underwater Acoust Antagonizing Lab Beijing 100036 Peoples R China 

出 版 物：《MATHEMATICAL PROBLEMS IN ENGINEERING》 (Math. Probl. Eng.)

年 卷 期：2014年第2014卷第1期

页      面：1-12页

核心收录：

学科分类：08[工学] 0701[理学-数学] 

基　　金：National Natural Science Foundation of China National Natural Science Foundation of Inner-Mongolia Autonomous Region China [2012MS0931] 

主　　题：TRACKING algorithms VISUAL perception MACHINE learning SUBMANIFOLDS DATA analysis 

摘      要：In the visual tracking scenarios, if there are multiple objects, due to the interference of similar objects, tracking may fail in the progress of occlusion to separation. To address this problem, this paper proposed a visual tracking algorithm with discrimination through multimanifold learning. Color-gradient-based feature tensor was used to describe object appearance for accommodation of partial occlusion. A prior multimanifold tensor dataset is established through the template matching tracking algorithm. For the purpose of discrimination, tensor distance was defined to determine the intramanifold and intermanifold neighborhood relationship in multimanifold space. Then multimanifold discriminate analysis was employed to construct multilinear projection matrices of submanifolds. Finally, object states were obtained by combining with sequence inference. Meanwhile, the multimanifold dataset and manifold learning embedded projection should be updated online. Experiments were conducted on two real visual surveillance sequences to evaluate the proposed algorithm with three state-of-the-art tracking methods qualitatively and quantitatively. Experimental results show that the proposed algorithm can achieve effective and robust effect inmulti-similar-object mutual occlusion scenarios.