A vision-based architecture for intent recognition

Authors:
Alireza Tavakkoli;Richard Kelley;Christopher King;Mircea Nicolescu;Monica Nicolescu;George Bebis
Affiliations:
Department of Computer Science and Engineering, University of Nevada, Reno;Department of Computer Science and Engineering, University of Nevada, Reno;Department of Computer Science and Engineering, University of Nevada, Reno;Department of Computer Science and Engineering, University of Nevada, Reno;Department of Computer Science and Engineering, University of Nevada, Reno;Department of Computer Science and Engineering, University of Nevada, Reno
Venue:
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Year:
2007

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Abstract

Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant for situations that involve collaboration among multiple agents or detection of situations that can pose a particular threat. We propose an approach that allows a physical robot to detect the intentions of others based on experience acquired through its own sensory-motor abilities. It uses this experience while taking the perspective of the agent whose intent should be recognized. The robot's capability to observe and analyze the current scene employs a novel vision-based technique for target detection and tracking, using a non-parametric recursive modeling approach. Our intent recognition method uses a novel formulation of Hidden Markov Models (HMM's) designed to model a robot's experience and its interaction with the world while performing various actions.