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2018 | OriginalPaper | Chapter

Learning Collaborative Sparse Correlation Filter for Real-Time Multispectral Object Tracking

Authors : Yulong Wang, Chenglong Li, Jin Tang, Dengdi Sun

Published in: Advances in Brain Inspired Cognitive Systems

Publisher: Springer International Publishing

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Abstract

To track objects efficiently and effectively in adverse illumination conditions even in dark environment, this paper presents a novel multispectral approach to deploy the intra- and inter-spectral information in the correlation filter tracking framework. Motivated by brain inspired visual cognitive systems, our approach learns the collaborative sparse correlation filters using color and thermal sources from two aspects. First, it pursues a sparse correlation filter for each spectrum. By inheriting from the advantages of the sparse representation, our filers are robust to noises. Second, it exploits the complementary benefits from two modalities to enhance each other. In particular, we take their interdependence into account for deriving the correlation filters jointly, and formulate it as a \({l}_{2,1}\)-based sparse learning problem. Extensive experiments on large-scale benchmark datasets suggest that our approach performs favorably against the state-of-the-arts in terms of accuracy while achieves in real-time frame rate.

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Title: Learning Collaborative Sparse Correlation Filter for Real-Time Multispectral Object Tracking
Authors: Yulong Wang
Chenglong Li
Jin Tang
Dengdi Sun
Publisher: Springer International Publishing
Book: Advances in Brain Inspired Cognitive Systems
Print ISBN: 978-3-030-00562-7

Electronic ISBN: 978-3-030-00563-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00563-4_45

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