Human Motion Trajectory Prediction: A Survey
It addresses the need for autonomous systems like self-driving vehicles and robots to anticipate human movements, but is incremental as a survey paper.
This paper surveys human motion trajectory prediction methods, analyzing and categorizing them based on motion modeling approaches and contextual information, while reviewing datasets and performance metrics.
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.