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

Structural sparse representation-based semi-supervised learning and edge detection proposal for... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Visual Computer Springer Journals

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

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision
ISSN
0178-2789
eISSN
1432-2315
D.O.I.
10.1007/s00371-016-1279-z
Publisher site
See Article on Publisher Site

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.

Journal

The Visual ComputerSpringer Journals

Published: Jun 4, 2016

References

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