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Structural sparse representation-based semi-supervised learning and edge detection proposal for visual tracking

Zhao, L and Zhao, Q and Liu, H and Lv, P and Gu, D (2017) 'Structural sparse representation-based semi-supervised learning and edge detection proposal for visual tracking.' Visual Computer, 33 (9). 1169 - 1184. ISSN 0178-2789

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Abstract

In discriminative tracking, lots of tracking methods easily suffer from changes of pose, illumination and occlusion. To deal with this problem, we propose a novel object tracking method using structural sparse representation-based semi-supervised learning and edge detection. First, the object appearance model is constructed by extracting sparse code features on different layers to exploit local information and holistic information. To utilize unlabelled samples information, the semi-supervised learning is introduced and a classifier is trained which is used to measure candidates. In addition, an auxiliary positive sample set is maintained to improve the performance of the classifier. We subsequently adopt an edge detection to alleviate the error accumulation based on the ranking results from the learned classifier. Finally, the proposed method is implemented under the Bayesian inference framework. Both the proposed tracker and several current trackers are tested on some challenging videos, where the target objects undergo pose change, illumination and occlusion. The experimental results demonstrate that the proposed tracker outperforms the other state-of-the-art methods in terms of effectiveness and robustness.

Item Type: Article
Uncontrolled Keywords: Structural sparse representation, Semi-supervised learning, Edge detection proposal, Object tracking
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 05 Sep 2017 14:25
Last Modified: 05 Sep 2017 15:15
URI: http://repository.essex.ac.uk/id/eprint/20322

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