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Optical Engineering • Open Access

Human tracking in thermal images using adaptive particle filters with online random forest learning
Author(s): Byoung Chul Ko; Joon-Young Kwak; Jae-Yeal Nam

Paper Abstract

This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented center-symmetric local binary patterns (OCS-LBP). Specifically, we design a real-time random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other methods.

Paper Details

Date Published: 18 November 2013
PDF: 15 pages
Opt. Eng. 52(11) 113105 doi: 10.1117/1.OE.52.11.113105
Published in: Optical Engineering Volume 52, Issue 11
Show Author Affiliations
Byoung Chul Ko, Keimyung Univ. (Korea, Republic of)
Joon-Young Kwak, Keimyung Univ. (Korea, Republic of)
Jae-Yeal Nam, Keimyung Univ. (Korea, Republic of)


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