A visual tracking framework for intent recognition in videos

Abstract

To function in the real world, a robot must be able to understand human intentions. This capability depends on accurate and reliable detection and tracking of trajectories of agents in the scene. We propose a visual tracking framework to generate and maintain trajectory information for all agents of interest in a complex scene. We employ this framework in an intent recognition system that uses spatio-temporal contextual information to recognize the intentions of agents acting in different scenes, comparing our system with the state of the art. © Springer-Verlag Berlin Heidelberg 2008.