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Authors: Mi Wang ; Huaxin Xiao ; Yu Liu ; Wei Xu and Maojun Zhang

Affiliation: National University of Defense, China

Keyword(s): Visual Tracking, Sparse and Low-Rank Representation.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Geometry and Modeling ; Image-Based Modeling ; Pattern Recognition ; Software Engineering ; Sparsity ; Theory and Methods

Abstract: Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. Numerous tracking methods using low-rank and sparse constraints perform well in visual tracking. However, these methods cannot reasonably balance the two characteristics. Sparsity always pursues a sparse enough solution that ignores the low-rank structure and vice versa. Therefore, this paper replaces the low-rank and sparse constraints with 2,1 l norm. A simplified lowrank and sparse model for visual tracking (LRSVT), which is built upon the particle filter framework, is proposed in this paper. The proposed method first prunes particles which are different with the object and selects candidate particles for efficiency. A dictionary is then constructed to represent the candidate particles. The proposed LRSVT algorithm is evaluated against three related tracking methods on a set of seven challenging image sequences. Experimental res ults show that the LRSVT algorithm favorably performs against state-of-the-art tracking methods with regard to accuracy and execution time. (More)

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Paper citation in several formats:
Wang, M.; Xiao, H.; Liu, Y.; Xu, W. and Zhang, M. (2017). A Simplified Low Rank and Sparse Model for Visual Tracking. In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-222-6; ISSN 2184-4313, SciTePress, pages 301-308. DOI: 10.5220/0006117003010308

@conference{icpram17,
author={Mi Wang. and Huaxin Xiao. and Yu Liu. and Wei Xu. and Maojun Zhang.},
title={A Simplified Low Rank and Sparse Model for Visual Tracking},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2017},
pages={301-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006117003010308},
isbn={978-989-758-222-6},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - A Simplified Low Rank and Sparse Model for Visual Tracking
SN - 978-989-758-222-6
IS - 2184-4313
AU - Wang, M.
AU - Xiao, H.
AU - Liu, Y.
AU - Xu, W.
AU - Zhang, M.
PY - 2017
SP - 301
EP - 308
DO - 10.5220/0006117003010308
PB - SciTePress

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