Video Event Classification and Detection Using 2d Trajectories

Abstract

Abstract:This paper describes an original statistical trajectory-based approach which can address several issues related to dynamic video content understanding: unsupervised clustering of events, recognition of events corresponding to learnt classes of dynamic video contents, and detection of unexpected events. Appropriate local differential features combining curvature and motion magnitude are robustly computed on the trajectories. They are invariant to image translation, in-the-plane rotation and scale transformation. The temporal causality of these features is then captured by hidden Markov models whose states are properly quantized values, and similarity between trajectories is expressed by exploiting the HMM framework. We report experiments on two sets of data, a first one composed of typical classes of synthetic (noised) trajectories (such as parabola or clothoid), and a second one formed with trajectories computed in sports videos. We have also favorably compared our method to other ones, including feature histogram comparison, use of the longest common subsequence (LCSS) distance and SVM-based classification.