  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.
