Behavior understanding at railway station by association of locational semantics and postures

The protection of critical transportation assets and infrastructure is an important topic in these days. In this paper, we develop a new rule based approach to smart video surveillance system for detecting situations where people may be in peril, as well as suspicious action or interactions at or near critical transportation assets. We analyze here three general types of human involved behaviors and interactions: (i) single pedestrian or no interaction, (ii) multiple pedestrian interactions, and (iii) pedestrian-facility/location interactions. The behavior analysis is accomplished through the development of geometric and motion visual features for each pedestrian. It is very simple and highly effective. The performance evaluation is carried out by using the video sequences taken in the real life environments of rail stations.