Multivariate adaptive gaussian mixture for scene level anomaly modeling

Abstract

Scene changes that typically occur in a real-world setting degrade anomaly detection performance over the long run. Most of the existing methods ignore the challenge of temporal concept drift in video surveillance. In this paper, we propose an unsupervised end-to-end framework for adaptive scene level anomaly detection. We utilize multivariate Gaussian mixtures for adaptive scene learning. The mixture represents the possible distribution of normal and abnormal events shown till now. The distribution adapts itself according to the slow scene changes. We introduce a Mahalanobis distance-based contribution factor to update mixture parameters on the arrival of each new event. A detailed discussion and experiments are conducted to decide optimum local as well as global temporal context. The existing public datasets for anomaly detection are of very short duration (maximum of 1.5 hours) to be used for evaluating adaptive approaches. Therefore we also collected a longer duration dataset of continuous 10 hours duration. We achieved a promising performance of 85.14% AUC and 21.26% EER on this data.