Anomaly event detection based on people trajectories for surveillance videos

In this work, we propose a novel approach to detect anomalous events in videos based on people movements, which are represented through time as trajectories. Given a video scenario, we collect trajectories of normal behavior using people pose estimation techniques and employ a multi-tracking data association heuristic to smooth trajectories. We propose two distinct approaches to describe the trajectories, one based on a Convolutional Neural Network and second based on a Recurrent Neural Network. We use these models to describe all trajectories where anomalies are those that differ much from normal trajectories. Experimental results show that our model is comparable with state-of-art methods and also validates the idea of using trajectories as a resource to compute one type of useful information to understand people behavior; in this case, the existence of rare trajectories.