Abstract

This paper presents a probabilistic grammar approach to the recognition of complex events in videos. Firstly, based on the origi- nal motion features, a rule induction algorithm is adopted to learn the event rules. Then, a multi-thread parsing (MTP) algorithm is adopted to recognize the complex events involving parallel temporal relation in sub-events, whereas the commonly used parser can only handle the se- quential relation. Additionally, a Viterbi-like error recovery strategy is embedded in the parsing process to correct the large time scale errors, such as insertion and deletion errors. Extensive experiments including indoor gymnastic exercises and outdoor traffic events are performed. As supported by experimental results, the MTP algorithm can effectively recognize the complex events due to the strong discriminative represen- tation and the error recovery strategy.