Learning motion trajectories via self-organization

This paper proposes a general framework for learning motion representations from low-level spatiotemporal features. The concept is based on a self-organizing map (SOM). The authors show how the SOM can be used for predicting object movements, and how additional information of the environment can be related to the inherent model of the movement to obtain generalized motion representations for objects. Traffic scenes are used to test the performance of the system