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Proceedings Paper

Low frame rate video target localization and tracking testbed
Author(s): Yu Pang; Dan Shen; Genshe Chen; Pengpeng Liang; Khanh Pham; Erik Blasch; Zhonghai Wang; Haibin Ling
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Paper Abstract

Traditional tracking frameworks are challenged by low video frame rate scenarios, because the appearances and locations of the target may change considerably in consecutive frames. Our paper presents a saliency-based temporal association dependency (STAD) framework to deal with such a low frame rate scenario and demonstrate good results in our robot testbed. We first use median filter to create a background of the scene, then apply background subtraction to every new frame to decide the rough position of the target. With the help of the markers on the robots, we use a gradient voting algorithm to detect the high responses of the directions of the robots. Finally, a template matching with branch pruning is used to obtain the finer estimation of the pose of the robots. To make the tracking-by-detection framework stable, we further introduce the temporal constraints using a previously detected result as well as an association technique. Our experiments show that our method can achieve a very stable tracking result and outperforms some state-of-the-art trackers such as Meanshift, Online-AdaBoosting, Mulitple-Instance-Learning, Tracking-Learning-Detection etc. Also. we demonstrate that our algorithm provides near real-time solutions given the low frame rate requirement.

Paper Details

Date Published: 22 May 2013
PDF: 8 pages
Proc. SPIE 8742, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IV, 87420T (22 May 2013); doi: 10.1117/12.2015954
Show Author Affiliations
Yu Pang, Temple Univ. (United States)
Dan Shen, Intelligent Fusion Technology, Inc. (United States)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)
Pengpeng Liang, Temple Univ. (United States)
Khanh Pham, Air Force Research Lab. (United States)
Erik Blasch, Air Force Research Lab. (United States)
Zhonghai Wang, Intelligent Fusion Technology, Inc. (United States)
Haibin Ling, Temple Univ. (United States)


Published in SPIE Proceedings Vol. 8742:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IV
Tien Pham; Michael A. Kolodny; Kevin L. Priddy, Editor(s)

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