Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

A dynamic model for real-time tracking of hands in bimanual movements

The problem of hand tracking in the presence of occlusion is addressed. In bimanual movements the hands tend to be synchronised effortlessly. Different aspects of this synchronisation are the basis of our research to track the hands. The spatial synchronisation in bimanual movements is modelled by the position and the temporal synchronisation by the velocity and acceleration of each hand. Based on a dynamic model, we introduce algorithms for occlusion detection and hand tracking