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Spatio-Temporal Auxiliary Particle Filtering With \ell _{1} -Norm-Based Appearance Model Learning for Robust Visual Tracking

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2 Author(s)
Du Yong Kim ; Sch. of Electr., Electron., & Comput. Eng., Univ. of Western Australia, Crawley, WA, Australia ; Moongu Jeon

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 l1-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.

Published in:

Image Processing, IEEE Transactions on  (Volume:22 ,  Issue: 2 )