A novel probabilistic approach utilizing clip attribute as hidden knowledge for event recognition

This paper proposes a novel probabilistic approach to utilize clip attributes as hidden knowledge for event recognition. Event recognition in surveillance videos is very challenging due to its large intra-class variations and relative low image resolution. The clip attributes, that are available only during training, provide auxiliary hidden information about the variation of the event appearance. Utilizing such hidden knowledge can help better model the joint probability distribution between event and its observations, and thus improve the recognition performance. We propose a probabilistic model to systematically incorporate the clip attributes into the event recognition. Experiments on real surveillance data show improved event recognition performance with the use of the clip attributes.