Granular trajectory based anomaly detection for surveillance
Francesco Maiorano, Alfredo Petrosino · 2016
Surveillance systems are widely spread in many public and private places of social life such as streets, offices, universities, hospitals and parking lots. The amount of data to be processed grows together with the need of skilled experts able to interpret it. For this reason, the scientific community put a great effort in conceiving approaches able to detect anomalies automatically. Whichever the systems that gather the data, be they heat sensors, cameras or gps trackers, anomalous event detection must rely on approaches able to promptly detect suspicious activities. To tackle this problem, we present an online point-wise approach to the analysis of spatio-temporal (trajectories) datasets in order to detect anomalous behaviors. We model trajectory dataset points as a Rough Set and use an outlier detection algorithm previously proposed. We perform a sliding temporal window scan to detect anomalies in real time. We demonstrate the robustness of this method using the CUHK crowd and the University of Udine trajectory datasets, which provide different combinations of trajectory scenarios: one group or multiple groups, one anomaly or multiple anomalies.