A HMM-Based Method for Recognizing Dynamic Video Contents from Trajectories

This paper describes an original method for classifying object motion trajectories in video sequences in order to recognize dynamic events. Similarities between trajectories are expressed from hidden Markov models representing each trajectory. We have favorably compared our method to several other ones, including histogram comparison, longest common subsequence distance and SVM classification. Trajectory features are computed from the curvature and velocity values at each point of the trajectory, so that they are invariant to translation, rotation and scale. We have evaluated our method on two sets of data, a first one composed of typical classes of synthetic trajectories (such as parabola or clothoid), and a second one formed with trajectories obtained by tracking cars in a Formula 1 race video.