Automatic Learning of Typical Paths in Video Surveillance

Abstract

This paper presents an approach to the acquisition of typical paths tread by people in large environments, and signal deviations. This work is part of a larger project, tested both in simulations as well as using real footage. The adaptation required for the continuous update of typical paths is achieved using Self-Organizing Maps (SOM), supplemented with original solutions for map-selection.