4 May 2009 Automatic tracking system with target classification
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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.
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Won-Chul Choi, Won-Chul Choi, Jik-Han Jung, Jik-Han Jung, Dong-Jo Park, Dong-Jo Park, Byung-In Choi, Byung-In Choi, Sungnam Choi, Sungnam Choi, } "Automatic tracking system with target classification", Proc. SPIE 7335, Automatic Target Recognition XIX, 73350T (4 May 2009); doi: 10.1117/12.819054; https://doi.org/10.1117/12.819054

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