Visual Detection of People Movement Rules Violation in Crowded Indoor Scenes

Abstract

The paper presents a camera-independent framework for detecting violations of two typical people movement rules that are in force in many public transit terminals: moving in the wrong direction or across designated lanes. Low-level image processing is based on object detection with Gaussian Mixture Models and employs Kalman filters with conflict resolving extensions for the object tracking. In order to allow an effective event recognition in a crowded environment, the algorithm for event detection is supplemented with the optical-flow based analysis in order to obtain pixel-level velocity characteristics. The proposed solution is evaluated with multi-camera, real-life recordings from an airport terminal. Results are discussed and compared with a traditional approach that does not include optical flow based direction of movement analysis. © Springer-Verlag Berlin Heidelberg 2013.