Deep Reinforcement Learning-based anomaly detection for Video Surveillance

Abstract

The anomaly detection in automated video surveillance is considered as one of the most critical tasks to be solved, in which we aim to detect a variety of real-world abnormalities. This paper introduces a novel approach for anomaly detection based on deep reinforcement learning. In recent years, deep reinforcement learning has been achieving a significant success in various applications with data of a high degree of complexity such as robotics and games, by mimicking the way humans learn from experiences. Generally, the state-of-the-art methods classify a video as normal or abnormal without pinpointing the exact location of the anomaly in the input video due to the unlabeled clip-level data in training videos. We focus on adapting the prioritized Dueling deep Q-networks to the anomaly detection problem. This model learns to evaluate the anomaly in video clips by exploiting the video-level label to obtain a better detection accuracy. Extensive experiments on 13 cases class of real-word anomaly show that our DRL agent achieved a near optimal performance with a high accuracy in the real world video surveillance system compared to the state-of-the-art approaches.