Journal of Systems Engineering and Electronics ›› 2009, Vol. 31 ›› Issue (9): 2266-2270.

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Nonrigid target tracking framework based on SVM and Mean-Shift

HOU Yi-min, LUN Xiang-min   

  1. School of Automation Engineering, Northeast Dianli Univ., Jilin 132012, China
  • Received:2008-06-03 Revised:2009-02-22 Online:2009-09-20 Published:2010-01-03

Abstract: To solve the problem of tracking nonrigid targets for image sequence in dynamic scene,a tracking framework based on support vector machine(SVM) and Mean-Shift is proposed.The rectangle in which the tracked target lies in is selected in the intial image,and the pixels in the given region around the target are taken as scene data.Then both the target and the scene pixel data are applied to train a two-value SVM classifier.The obtained SVM classifier is employed to classify the pixel data lying in the same region of the next image,thus the confidence map including two pixel values is got.The Mean-Shift algorithm is performed to get the position of the current target within the area of the confidence map.Similarly,the new SVM classifiers are trained for tracking the images to be followed until the whole image sequence tracking is completed.The experiment results show that the proposed method is suitable for tracking nonrigid targets and active scenes,and has the real-time characteristic.

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