关键词: 视频异常检测, 弱监督, 视频级特征, 时空注意力, 加权平均特征

Abstract: Video Anomaly Detection (VAD) has become an important task in the field of computer vision.Currently, Weakly-supervised Video Anomaly Detection (WVAD) has become one of the mainstream methods for video anomaly detection.However, the existing methods suffer from the problem of treating each video snipper as an independent and identically instance, ignoring the spatio-temporal dependencies at the video level.To address the problem,Three-Dimensional rearrangement MultiLayer Perceptron driven Cross-Dimensional Feature Interaction for Weakly-supervised Video Anomaly Detection(rMLP-WVAD)is proposed.First, multi-scale features of video frames are extracted using I3D encoder, and the features are feature-enhanced by 3D rearrangement of MLP-driven Video-level feature interaction and Spatio-temporal Attention (VSA) to preserve video-level spatio-temporal dependencies and to fully retain key spatio-temporal contextual information.Then, as the cross-dimensional features are further mined and enriched, how to define and quantify "anomaly" more accurately becomes the key to effectively detecting abnormal events.For this reason,the Divergence of Feature from Weighted Mean vector (DFWM) is proposed as an anomaly criterion to fully utilize the enhanced spatio-temporal feature representation and quantify anomalies more accurately and improve the performance of detection.Finally, the experimental results on public datasets show that rMLP-WVAD achieves an AP of 86.39% on the XD-Violence dataset, and the AUC on the UCF-Crime dataset reaches 85.70%, which verifies the validity of the method.

Key words: Video Anomaly Detection, Weak-supervised, Weighted Features, Video-grade features, Spatio-Temporal Attention