title: Spatio-temporal auxiliary particle filtering with l1-norm-based appearance model learning for robust visual tracking.
abstract: In this paper, we propose an efficient and accurate visual tracker equipped with a new particle filtering algorithm and robust subspace learning-based appearance model. The proposed visual tracker avoids drifting problems caused by abrupt motion changes and severe appearance variations that are well-known difficulties in visual tracking. The proposed algorithm is based on a type of auxiliary particle filtering that uses a spatio-temporal sliding window. Compared to conventional particle filtering algorithms, spatio-temporal auxiliary particle filtering is computationally efficient and successfully implemented in visual tracking. In addition, a real-time robust principal component pursuit (RRPCP) equipped with  l(1)-norm optimization has been utilized to obtain a new appearance model learning block for reliable visual tracking especially for occlusions in object appearance. The overall tracking framework based on the dual ideas is robust against occlusions and out-of-plane motions because of the proposed spatio-temporal filtering and recursive form of RRPCP. The designed tracker has been evaluated using challenging video sequences, and the results confirm the advantage of using this tracker.
authors.affiliation: School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju 500-712, Korea. duyongkim@gmail.com
authors.forename: Du Yong
authors.initials: du Y
authors.lastname: Kim
authors.forename: Moongu
authors.initials: M
authors.lastname: Jeon