关键词: 深度学习, 视频异常检测, 无监督学习, 姿态感知, 跨模态

Abstract: Existing unsupervised Video Anomaly Detection (VAD) methods suffer from two critical limitations. First, high-level semantic information from human poses has not been effectively exploited, resulting in insufficient sensitivity to anomalies in behavioral logic. Second, memory modules are readily disturbed during training by multimodal feature interference and latent noise contamination in the data, which compromises the purity and robustness of prototype learning. To address these issues, a two-stage prototype memory framework based on Pose Awareness and cross-modal Motion Consistency Network (PAMC-Net) was proposed. A four-branch video decomposition scheme was constructed to separately model motion, scene, object, and pose information, and an independent prototype learning-and-freezing training paradigm was designed to preserve the purity of motion and pose prototypes while preventing mutual feature interference. In addition, a cross-modal consistency loss between pose and motion was introduced to incorporate physical plausibility constraints into unsupervised learning, enabling the detection of anomalous behaviors that violate human kinematics. Extensive experiments were conducted on four widely used benchmark datasets — CUHK Avenue, UCSD Ped1, UCSD Ped2, and ShanghaiTech — and the proposed framework achieves Area Under Curve (AUC) scores of 90.1%, 86.4%, 98.7%, and 76.6%, respectively. Compared with MS2NL (Multi-scale Spatiotemporal Normality Learning), an improvement of 1.7 percentage points in AUC is obtained by the proposed framework on the ShanghaiTech dataset. These results indicate that the proposed framework addresses the challenges posed by anomalous human behaviors in VAD effectively.

Key words: deep learning, Video Anomaly Detection (VAD）, unsupervised learning, posture detection, cross-modal