Understanding Sports Video Using Players Trajectories

One of the main goal for novel machine learning and computer vision systems is to perform automatic video event understanding. In this chapter, we present a content-based approach for understanding sports videos using players trajectories. To this aim, an object-based approach for temporal analysis of videos is described. An original hierarchical parallel semi-Markov model (HPaSMM) is proposed. In this latter, a lower level is used to model players trajectories motions and interactions using parallel hidden Markov models, while an upper level relying on semi-Markov chains is considered to describe activity phases. Such probabilistic graphical models help taking into account low level temporal causalities of trajectories features as well as upper level temporal transitions between activity phases. Hence, it provides an efficient and extensible machine learning tool for applications of sports video semantic-based understanding such that segmentation, summarization and indexing. To illustrate the efficiency of the proposed modeling, application of the novel modeling to two sports, and the corresponding results, are reported.