TR#423: Human Action Detection Using PNF Propagation of Temporal Constraints

Claudio Pinhanez and Aaron Bobick

Submitted to ICCV'98

In this paper we develop a representation for the temporal structure inherent in human actions and demonstrate an effective method for using that representation to detect the occurrence of actions. The temporal structure of the action, sub-actions, events, and sensor information is described using a constraint network based on Allen's interval algebra. We map these networks onto a simpler, 3-valued domain (past,now,future) network --- a PNF-network --- to allow fast detection of actions and sub-actions. The occurrence of an action is computed by considering the minimal domain of its PNF-network, under constraints imposed by the current state of the sensors and the previous states of the network. We illustrate the approach with examples, showing that a major advantage of PNF propagation is the detection and removal of situations inconsistent with the temporal structure of the action. We also examine a method to increase the robustness of PNF propagation in the case of faulty sensors.