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Proceedings Paper

Infrared target tracking using multiple instance learning with adaptive motion prediction and spatially template weighting
Author(s): Xinchu Shi; Weiming Hu; Yun Cheng; Genshe Chen; Jingjing Ji; Haibin Ling
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Paper Abstract

In this paper, we formulate the problem of infrared target tracking as a binary classification task and extend the online multiple instance learning tracker (MILTracker) for the task. Compared with many color or texture based tracking algorithms, the MILtracker highlights the difference between the target and the background or similar objects, and is thus suitable for infrared target tracking which undergoes serious textual information loss. To address the specific challenges in the infrared sequences, we extend the original MILtracker from two aspects. Firstly, an adaptive motion prediction procedure is integrated in to enhance the efficiency of the tracker. This step helps discriminate disturbing objects that are visual very similar to the target under tracking. Secondly, a spatial weight mask is introduced into the target representation to augment its robustness against similar background clutters, especially distracters. We apply the proposed approach on several challenging IR sequences. The experimental results clearly validate the effectiveness of our method with encouraging performances.

Paper Details

Date Published: 1 October 2013
PDF: 7 pages
Proc. SPIE 8739, Sensors and Systems for Space Applications VI, 873912 (1 October 2013); doi: 10.1117/12.2015614
Show Author Affiliations
Xinchu Shi, Institute of Automation (China)
Temple Univ. (United States)
Weiming Hu, Institute of Automation (China)
Yun Cheng, Hunan Univ. of Humanities, Science and Technology (China)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)
Jingjing Ji, Temple Univ. (United States)
Haibin Ling, Temple Univ. (United States)

Published in SPIE Proceedings Vol. 8739:
Sensors and Systems for Space Applications VI
Khanh D. Pham; Joseph L. Cox; Richard T. Howard; Genshe Chen, Editor(s)

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