Multi-thread Parsing for Recognizing Complex Events in Videos

  • Authors:

  • Zhang Zhang;Kaiqi Huang;Tieniu Tan

  • Affiliations:

  • National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing, China 100190;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing, China 100190;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing, China 100190

  • Venue:
  • ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
  • Year:
  • 2008

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Abstract

This paper presents a probabilistic grammar approach to the recognition of complex events in videos. Firstly, based on the original 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 sequential 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 representation and the error recovery strategy.