ICRA 2011 Paper Abstract

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Paper WeP214.3

Bai, Tianxiang (City University of Hong Kong), Li, Y.F. (City University of Hong Kong), TANG, Yazhe (City University of Hong Kong)

Structured Sparse Representation Appearance Model for Robust Visual Tracking

Scheduled for presentation during the Regular Sessions "Visual Tracking" (WeP214), Wednesday, May 11, 2011, 15:55−16:10, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 8, 2019

Keywords Visual Tracking

Abstract

We propose a robust visual tracker based on structured sparse representation appearance model. The appearance of tracking target is modeled as a sparse linear combination of Eigen templates plus a sparse error due to occlusions. We address the structured sparse representation that preferably matches the practical visual tracking problem by taking the contiguous spatial distribution of occlusion into account. The sparsity is achieved by Block Orthogonal Matching Pursuit (BOMP) for solving structured sparse representation problem more efficiently. The model update scheme, based on incremental Singular Value Decomposition (SVD), guarantees the Eigen templates that are able to capture the variations of target appearance online. Then the approximation error is adopted to build a probabilistic observation model that integrates with a stochastic affine motion model to form a particle filter framework for visual tracking. Thanks to the block structure of sparse representation and BOMP, our proposed tracker demonstrates superiority on both efficiency and robustness improvement in comparison experiments with publicly available benchmark video sequences.

 

 

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