Incremental Activity Modelling in Multiple Disjoint Cameras

Activity modelling and unusual event detection in a network of cameras is challenging particularly when the camera views are not overlapped. We show that it is possible to detect unusual events in multiple disjoint cameras as context-incoherent patterns, through incremental learning of time delayed dependencies between distributed local activities observed within and across camera views. Specifically, we model multi-camera activities using a Time Delayed Probabilistic Graphical Model (TD-PGM) with different nodes representing activities in different decomposed regions from different views and the directed links between nodes encoding their time delayed dependencies. To deal with visual context changes, we formulate a novel incremental learning method for modelling time delayed dependencies that change over time. We validate the effectiveness of the proposed approach using a synthetic dataset and videos captured from a camera network installed at a busy underground station.