Open AccessRobust multi‐feature visual tracking via multi‐task kernel‐based sparse learning
Author(s) -
Kang Bin,
Zhu WeiPing,
Liang Dong
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.1062
Subject(s) - artificial intelligence , sparse approximation , computer science , pattern recognition (psychology) , robustness (evolution) , kernel (algebra) , feature selection , eye tracking , feature learning , computer vision , feature (linguistics) , mathematics , linguistics , philosophy , combinatorics , biochemistry , chemistry , gene
Feature selection and fusion is of crucial importance in multi‐feature visual tracking. This study proposes a multi‐task kernel‐based sparse learning method for multi‐feature visual tracking. The proposed sparse learning method can discriminate the reliable and unreliable features for optimal multi‐feature fusion through using a Fisher discrimination criterion‐based multi‐objective model to adaptively train the kernel weights of different features such as pixel intensity, edge and texture. To guarantee a robustness of the sparse representation method, a mixed norm is employed in the sparse leaning method to adaptively select correlated particle observations for multi‐task sparse reconstruction. Experimental results show that the proposed sparse learning method can achieve a better tracking performance than state‐of‐the‐art tracking methods do.