Sunday 24th of September 2017
 

MAS for video objects segmentation and tracking based on active contours and SURF descriptor


Mohamed Chakroun, Ali Wali and Adel M. Alimi

In computer vision, the problem of segmentation and tracking is of great importance. In this paper, we describe a novel video sequences segmentation and tracking system based on MAS \multi-agent systems\ and SURF \Speeded Up Robust Features\. This system can be used to index and search the video sequence by the visual content. Our system consists in modeling a multi-agent system for segmenting the first image from a video sequence and tracking objects in the video sequences. The used agents are supervisor and explorator agents, they communicate between them and they inspire in their behavior from active contours approaches. The tracking of objects is based on SURF descriptors \Speed Up Robust Features\.In computer vision, video segmentation and tracking is an important challenging issue. In this paper, we describe a new video sequences segmentation and tracking algorithm based on MAS multi-agent systems and SURF Speeded Up Robust Features. Our approach consists in modelling a multi-agent system for segmenting the first image from a video sequence and tracking objects in the video sequences. The used agents are supervisor and explorator agents, they are communicating between them and they inspire in their behavior from active contours approaches. The tracking of objects is based on SURF descriptors Speed Up Robust Features. We used the DIMA platform and API Ateji PX (an extension of the Java language to facilitate parallel programming on heterogeneous architectures) to implement this algorithm. The experimental results indicate that the proposed algorithm is more robust and faster than previous approaches.

Keywords: segmentation; tracking; video; multi-agent; SURF; active contour

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ABOUT THE AUTHORS

Mohamed Chakroun
REGIM: REsearch Group on Intelligent Machines

Ali Wali
REGIM: REsearch Group on Intelligent Machines

Adel M. Alimi
REGIM: REsearch Group on Intelligent Machines


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