Spatio-temporal auxiliary particle filtering with l<inf>1</inf>-Norm- Based Appearance Model Learning for Robust Visual Tracking
|dc.contributor.author||Kim, Du Yong|
|dc.identifier.citation||Kim, D.Y. and Jeon, M. 2013. Spatio-temporal auxiliary particle filtering with l<inf>1</inf>-Norm- Based Appearance Model Learning for Robust Visual Tracking. IEEE Transactions on Image Processing. 22 (2): pp. 511-522.|
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. © 1992-2012 IEEE.
|dc.title||Spatio-temporal auxiliary particle filtering with l<inf>1</inf>-Norm- Based Appearance Model Learning for Robust Visual Tracking|
|dcterms.source.title||IEEE Transactions on Image Processing|
|curtin.department||Department of Electrical and Computer Engineering|
|curtin.accessStatus||Fulltext not available|
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