STA-VAD: Video Anomaly Detection Utilizing Spatio-Temporal Memory and Adaptive Deformation

Classical unsupervised video anomaly detection methods learn normal patterns from normal video frames and assume significant reconstruction deviations for unforeseen anomalous video frames. These methods often introduce additional constraints to distinguish anomalies, potentially leading to higher reconstruction errors for normal instances. While specific methods have alleviated this issue by estimating multiscale deformation fields, these rely on additional background information that is susceptible to changes in the real world. In this paper, we propose a spatio-temporal relationship-based video anomaly detection STA-VAD method. By emphasizing spatio-temporal relationships in videos, our model better captures contextual information, reducing the impact of irrelevant background information. Additionally, we enhance the multiscale deformation field by adaptively adjusting it to quantify the severity of anomalies in video frames, thereby improving discrimination between normal and anomalous samples. In the widely recognized video anomaly detection datasets UCSD Ped2 and CUHK Avenue, our method demonstrates significant enhancements in accuracy and reliability, achieving state-of-the-art performance overall.