Motion feature filtering for event detection in crowded scenes

We describe a spatio-temporal feature filtering approach that is appropriate for detecting video events in public scenes containing from many to few people. This non-discrete tracking - or pattern flow analysis - is distinguished by the fact that the usual video processing step of object segmentation is omitted; instead motion features alone are used to detect, follow, and separate activity. Motion features include location, scale, score ( magnitude), direction, and velocity. The method entails gradient-based motion detection and multiscale motion feature calculation to obtain a scene activity vector. We focus on obtaining these motion features and filtering them to obtain information on activity, with the end-goal being event detection, classification, and anomaly detection. Examples of information extraction we show in this paper include: distinguishing anomalous from trend activity via shape of the activity profile over time, detecting event onset and direction of people flow using direction ( and feature confidence) values, and measuring the periodicity of similar activity from magnitude values over time. We demonstrate utility of the approach on 3 video datasets: hallway, emergency event, and subway platform. (C) 2013 Elsevier B.V. All rights reserved.