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Object Detection and Segmentation  Using Adaptive MeanShift Blob  Tracking Algorithm and Graph Cuts  Theory    
  • adnane


In this paper, we present method of detection, segmentation and tracking to different objects in video sequence in real-time. We propose new approach based on Blob tracking, the technique, we find a hybrid combination between tracking-detection, in blob tracking use detection model based on two pieces of information; brightness and color. Our approach adds new properties in these blobs based on shape features extractions, where we de- fine several properties for efficient detection. These blobs, present objects detected, the motion is estimated by non-parametric Kernel density estimation by using MeanShift algorithm to track this blobs. Segmentation is performed by GraphCuts approach; it generates and updates a set of Blobs in the sequence. Experimental results demonstrate that our method is robust for challenging data and present many advantages inside other approaches.