Complex Activity Representation and Recognition by Extended Stochastic Grammar

Stochastic grammar has been used in many video analysis and event recognition applications as an efficient model to represent large-scale video activity. However, in previous works, due to the limitation on representing parallel temporal relations, traditional stochastic grammar cannot be used to model complex multi-agent activity including parallel temporal relations between subactivities (such as "during" relation). In this paper, we extend the traditional grammar by introducing Temporal Relation Events (TRE) to solve the problem. The corresponding grammar parser appending complex temporal inference is also proposed. A system that can recognize two hands' cooperative action in a "telephone calling" activity is built to demonstrate the effectiveness of our methods. In the experiment, a simple method to model the explicit state duration probability distribution in HMM detector is also proposed for accurate primitive events detection.

• Type: Conference
• Year: 2006
• Where: ACCV
• Authors: Zhang Zhang, Kaiqi Huang, Tieniu Tan
• Keywords: ACCV 2006, Computer Vision, Parallel Temporal Relations, Stochastic Grammar, Temporal Relation