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Open AccessArticle

A Parallel Search Strategy Based on Sparse Representation for Infrared Target Tracking

Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China
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Academic Editor: Jun-Bao Li
Algorithms 2015, 8(3), 529-540; https://doi.org/10.3390/a8030529
Received: 24 April 2015 / Revised: 15 June 2015 / Accepted: 1 July 2015 / Published: 27 July 2015
(This article belongs to the Special Issue Machine Learning Algorithms for Big Data)
A parallel search strategy based on sparse representation (PS-L1 tracker) is proposed in the particle filter framework. To obtain the weights of state particles, target templates are represented linearly with the dictionary of target candidates. Sparse constraints on the coefficient guarantee that only true target candidates can be selected, and the nonnegative entries denote the associate weights of efficient target states. Then the optimal target state can be estimated by the linear combination of above weighted states. In this way, efficient target states are selected simultaneously from all the particles, which we call a parallel search strategy. Experimental results demonstrate excellent performance of the proposed method on challenging infrared images. View Full-Text
Keywords: infrared target tracking; sparse representation; parallel search infrared target tracking; sparse representation; parallel search
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Shi, Z.; Wei, C.; Fu, P.; Jiang, S. A Parallel Search Strategy Based on Sparse Representation for Infrared Target Tracking. Algorithms 2015, 8, 529-540.

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