Video surveillance has been an area of significant interest in both academia and industry. Our focus is on problems, where we are given a set of nominal training videos samples. Based on these samples we need to determine whether or not a test video contains an anomaly.
Anomalies in many video surveillance applications have local spatio-temporal signatures, namely, they occur over a small time window or a small spatial region. The distinguishing feature of these scenarios is that outside this spatio-temporal anomalous region, activities appear normal. We develop a probabilistic framework to account for such local spatio-temporal anomalies. We show that our framework admits elegant characterization of optimal decision rules. A key insight of the paper is that if anomalies are local optimal decision rules are local even when the nominal behavior exhibits global spatial and temporal statistical dependencies. This insight helps collapse the large ambient data dimension for detecting local anomalies. Consequently, consistent data-driven local empirical rules with provable performance can be derived with limited training data. Our empirical rules are based on scores functions derived from local nearest neighbor distances. These rules aggregate statistics across spatio-temporal locations & scales,and produce a single composite score for video segments.We demonstrate the efficacy of our scheme on several video surveillance data sets and compare with existing work.
Abnormal event detections from some data sets. The objects such as cars, bicycles, skaters, u-turns are all well detected.
V. Saligrama, Z. Chen, Video Anomaly Detection Based on Local Statistical Aggregates, CVPR 2012