Detection of anomalous track patterns for long term surveillance

Surveillance cameras have become ubiquitous in society, used to monitor areas such as residential blocks, city streets, university campuses, industrial sites, and government installations. Surveillance footage, especially of public areas, is frequently streamed online in real time, providing a wealth of data for computer vision research. The focus of this work is on detection of anomalous patterns in surveillance video data recorded over a period of months to years. We propose an anomaly detection technique based on support vector data description (SVDD) to detect anomalous patterns in video footage of a university campus scene recorded over a period of months. SVDD is a kernel-based anomaly detection technique which models the normalcy data in a high dimensional feature space using an optimal enclosing hypersphere – samples that lie outside this boundary are detected as outliers or anomalies. Two types of anomaly detection are conducted in this work: track-level analysis to determine individual tracks that are anomalous, and day-level analysis using aggregate scene level feature maps to determine which days exhibit anomalous activity. Experimentation and evaluation is conducted using a scene from the Global Webcam Archive.