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Computational Intelligence and Neuroscience
Volume 2017, Article ID 2426475, 7 pages
https://doi.org/10.1155/2017/2426475
Research Article

Patch Based Multiple Instance Learning Algorithm for Object Tracking

1Department of Information Engineering and Automation, Hebei College of Industry and Technology, Shijiazhuang, China
2Faculty of Electrical & Electronics Engineering, Shijiazhuang University of Applied Technology, Shijiazhuang, China

Correspondence should be addressed to Lijia Wang; moc.liamtoh@1891aijilgnaw

Received 17 September 2016; Revised 19 December 2016; Accepted 9 January 2017; Published 22 February 2017

Academic Editor: Silvia Conforto

Copyright © 2017 Zhenjie Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object. In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs. After detecting the object, the learning rates for updating weak classifiers’ parameters are adaptively tuned. The classifier updating strategy avoids overupdating and underupdating the parameters. Finally, the proposed method is compared with other state-of-the-art algorithms on several classical videos. The experiment results illustrate that the proposed method performs well especially in case of illumination changes or pose variations and partial occlusion. Moreover, the algorithm realizes real-time object tracking.