Online visual tracking based on selective sparse appearance model and spatiotemporal analysis

Ming Xue; Shibao Zheng; Hua Yang; Yi Zhou; Zhenghua Yu

doi:10.1117/1.OE.53.1.013103

23 January 2014 Online visual tracking based on selective sparse appearance model and spatiotemporal analysis

Ming Xue, Shibao Zheng, Hua Yang, Yi Zhou, Zhenghua Yu

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Optical Engineering, Vol. 53, Issue 1, 013103 (January 2014). https://doi.org/10.1117/1.OE.53.1.013103

Abstract

To tackle robust visual tracking in complex environment, an online algorithm based on generative model is proposed. The target is represented with overlapped and selected local patches based on key point proportion ranking, and its location is estimated by spatiotemporal analysis. Temporally, a propagated affine warping dynamical model is newly introduced. Spatially, an observation model based on weighted sparse representation and geometric confidence inference is newly established. Both selection pattern and templates are periodically updated to adapt the target’s appearance variation. Experiments demonstrate that the proposed approach achieves more favorable performance compared with classical works on challenging image sequences.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Ming Xue, Shibao Zheng, Hua Yang, Yi Zhou, and Zhenghua Yu "Online visual tracking based on selective sparse appearance model and spatiotemporal analysis," Optical Engineering 53(1), 013103 (23 January 2014). https://doi.org/10.1117/1.OE.53.1.013103

Published: 23 January 2014

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