In this paper, we propose an original approach for content-based video indexing and retrieval. It relies on the tracking of entities of interest and the analysis of their apparent motion. To characterize the dynamic information attached to these objects, we consider a probabilistic modeling of the spatio-temporal distribution of the optic flow field computed within the tracked area after canceling the estimated dominant motion due to camera movement. This leads to a general statistical framework for motion-based video classification and retrieval. We have obtained promising results on a set of various real image sequences.