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

Conference paper

DOI: 10.1007/978-3-319-01622-1_17

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 233)
Cite this paper as:
Mohcine B., Benayad N. (2014) Object Detection and Segmentation Using Adaptive MeanShift Blob Tracking Algorithm and Graph Cuts Theory. In: S. Choras R. (eds) Image Processing and Communications Challenges 5. Advances in Intelligent Systems and Computing, vol 233. Springer, Heidelberg

Summary

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 define 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.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.LIAD. Faculty of SciencesHassan II UniversityMaarifMorocoo

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