Target Tracking Algorithm Based on HOG Feature and Sparse Representation

DOI

10.2991/icamcs-16.2016.87How to use a DOI?

Keywords

visual tracking, HOG feature, sparse representation, classifier

Abstract

In this paper, we propose a novel algorithm to deal with the problem of visual tracking in some challenging situations, which is based on HOG feature and sparse representation. First of all, describe target according to the HOG feature; secondly, construct the appearance model of target with the sparse representation, and then predict the target position on the basis of the particle filter method. At last, apply Naive Bayes classifier to track target. The experiment results show that the proposed algorithm is superior in accuracy than the classical tracking algorithm and has better robustness in the scene that contains the target posture changes, illumination variations and occlusion.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ming Li
AU  - Qingsong Fang
PY  - 2016/06
DA  - 2016/06
TI  - Target Tracking Algorithm Based on HOG Feature and Sparse Representation
BT  - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
PB  - Atlantis Press
SP  - 411
EP  - 416
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamcs-16.2016.87
DO  - 10.2991/icamcs-16.2016.87
ID  - Li2016/06
ER  -