Multi-Scale Temporal Relations and Segmented Channel Attention for Video Anomaly Detection

Abstract

In recent years, the rapid advancement in video surveillance technology has significantly enhanced public safety and security. In conventional video anomaly detection approaches, there is often an exclusive focus on local information, with key temporal dynamics being overlooked. This oversight could potentially lead to a failure in recognizing dynamic anomalies, such as the sudden running of a person or the rapid movement of objects. Therefore, this study proposes a model framework structure called MTR-SCA. By utilizing widerresnet38 and Multi-Scale Temporal Relations (MTR) to capture the multi-scale temporal relationships in video time series, the framework achieves an understanding of spatial and temporal information. It introduces the Segmented Channel Attention (SCA) to enhance key information in the input feature maps and suppress less important channels for refined feature selection. We conducted experiments with the MTR-SCA network on three datasets: Avenue, ped2, and ShanghaiTech, achieving results of 97.8%, 86.8%, and 74.1% respectively.