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Proceedings Paper

Automatic tracking system with target classification
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Paper Abstract

In this paper, we propose an overall target tracking scheme performing image stabilization, detection, tracking, and classification in the IR sensored image. Firstly, in the image stabilization stage, a captured image is stabilized from visible frame-to-frame jitters caused by camera shaking. After that, the background of the image is modeled as Gaussian. Based on the results of the background modeling, the difference image between a Gaussian background model and a current image is obtained, and regions with large differences are considered as targets. The block matching method is adopted as a tracker, which uses the image captured from the detected region as a template. During the tracking process, positions of the target are compensated by the Kalman filter. If the block matching tracker fails to track targets as they hide themselves behind obstacles, a coast tracking method is employed as a replacement. In the classification stage, key points are detected from the tracked image by using the scale-invariant feature transform (SIFT) and key descriptors are matched to those of pre-registered template images.

Paper Details

Date Published: 4 May 2009
PDF: 8 pages
Proc. SPIE 7335, Automatic Target Recognition XIX, 73350T (4 May 2009); doi: 10.1117/12.819054
Show Author Affiliations
Won-Chul Choi, Korea Advanced Institute of Science and Technology (Korea, Republic of)
Jik-Han Jung, Korea Advanced Institute of Science and Technology (Korea, Republic of)
Dong-Jo Park, Korea Advanced Institute of Science and Technology (Korea, Republic of)
Byung-In Choi, Samsung Thales Co., Ltd. (Korea, Republic of)
Sungnam Choi, Samsung Thales Co., Ltd. (Korea, Republic of)


Published in SPIE Proceedings Vol. 7335:
Automatic Target Recognition XIX
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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