Detection of highly articulated moving objects by using co-segmentation with application to athletic video sequences

In this paper, we adapt the co-segmentation for the fundamental problem of segmenting automatically and accurately highly articulated athletes in a large variety of poses without any initialization or prior knowledge. Our intention is to reduce the complexity of athlete segmentation by formulating it as a constrained 2D pair of frames’ co-segmentation, in order to extract the common foreground objects under unconstrained environments without any user input. In fact, the co-segmentation allows to integrate implicitly the temporal information for automatic moving object segmentation without any assumption or prior knowledge on camera motion. The proposed method was applied on various real-world video sequences of athletic sports meetings, and promising results are obtained. Experiments show that suggested method witnessed a significant improvement over background subtraction methods, which are commonly used for athlete segmentation.