Key-region representation learning for anomaly detection

Abstract

Anomaly detection and localization is of great importance for public safety monitoring. In this paper we focus on individual behavior anomaly detection, which remains a challenging problem due to complicated dynamics of video data. We try to solve this problem in a way based on feature extraction, we believe that patterns are easier to classify in feature space. However, different from many works in video analysis, we only extract features from small key-region patches, which allows our feature extraction module to have a simple architecture and be more targeted at anomaly detection. Our anomaly detection framework consists of three parts, the main part is an auto-encoder based representation learning module, and the other two parts, key-region extracting module and Mahalanobis distance based classifier, are specifically designed for anomaly detection in video. Our work has the following advantages: (1) our anomaly detection framework focus only on suspicious regions, and can detect anomalies with high accuracy and speed. (2) Our anomaly detection classifier has a stronger power to capture data distribution for anomaly detection.