A Simplified Low Rank and Sparse Model for Visual Tracking

Mi Wang, Huaxin Xiao, Yu Liu, Wei Xu, Maojun Zhang

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 results show that the LRSVT algorithm favorably performs against state-of-the-art tracking methods with regard to accuracy and execution time.

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Paper Citation


in Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 301-308. DOI: 10.5220/0006117003010308


in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2017},
pages={301-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006117003010308},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Simplified Low Rank and Sparse Model for Visual Tracking
SN - 978-989-758-222-6
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