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Robust visual tracking via randomly projected instance learning

Fei Cheng (School of Computer Science and Technology, Xidian University, Xi’an, China)
Kai Liu (School of Computer Science and Technology, Xidian University, Xi’an, China)
Mao-Guo Gong (School of Electronic Engineering, Xidian University, Xi’an, China)
Kaiyuan Fu (School of Computer Science and Technology, Xidian University, Xi’an, China)
Jiangbo Xi (School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA)

International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 14 August 2017

131

Abstract

Purpose

The purpose of this paper is to design a robust tracking algorithm which is suitable for the real-time requirement and solves the mistake labeling issue in the appearance model of trackers with the spare features.

Design/methodology/approach

This paper proposes a tracker to select the most discriminative randomly projected ferns and integrates a coarse-to-fine search strategy in this framework. First, the authors exploit multiple instance boosting learning to maximize the bag likelihood and select randomly projected fern from feature pool to degrade the effect of mistake labeling. Second, a coarse-to-fine search approach is first integrated into the framework of multiple instance learning (MIL) for less detections.

Findings

The quantitative and qualitative experiments demonstrate that the tracker has shown favorable performance in efficiency and effective among the competitors of tracking algorithms.

Originality/value

The proposed method selects the feature from the compressive domain by MIL AnyBoost and integrates the coarse-to-fine search strategy first to reduce the burden of detection. This paper designs a tracker with high speed and favorable results which is more suitable for real-time scene.

Keywords

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 61571345, the Fundamental Research Funds for the Central Universities under Grant No. K5051203005, and the National Natural Science Foundation of China under Grant No. 6150110247.

Citation

Cheng, F., Liu, K., Gong, M.-G., Fu, K. and Xi, J. (2017), "Robust visual tracking via randomly projected instance learning", International Journal of Intelligent Computing and Cybernetics, Vol. 10 No. 3, pp. 258-271. https://doi.org/10.1108/IJICC-11-2016-0052

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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