Anomaly Detection for Video Surveillance

Abstract

Anomaly detection in video surveillance is critical for identifying abnormal behaviours or events that deviate from typical patterns. This abstract explores various techniques used to detect anomalies in surveillance videos, including traditional methods like motion detection and background subtraction, as well as advanced approaches such as deep learning and anomaly scoring algorithms. By leveraging these techniques, surveillance systems can automatically detect suspicious activities such as trespassing, theft, or violence, enabling timely intervention and improved security measures. Highlighting the challenges in anomaly detection, such as dealing with complex scenes, occlusions and discusses ongoing research efforts to enhance the accuracy and efficiency of anomaly detection systems in real-world surveillance scenarios. Key Words: anomaly, suspicious, pattern, motion, detection, security measure