新方法结合YOLO和CLIP，无需光流和独立姿态估计器，实时检测异常行为，速度提升3倍多。
研究人员提出了一种轻量级两阶段框架，用于实时视频异常检测。第一阶段使用YOLO v11n-pose检测人物并提取17个骨骼关键点。第二阶段通过CLIP ViT-B/32编码每个裁剪的人物区域，并计算与预定义异常行为文本描述的余弦相似度。该架构在NVIDIA Titan XP GPU上实现了约51 FPS的端到端吞吐量，比多特征基线快3.36倍，同时在CUHK Avenue、ShanghaiTech Campus和朱拉隆功大学自建数据集上分别保持了89.26%、70.26%和84.13%的帧级AUROC值。
Real-Time Video Anomaly Detection Using YOLO Pose Estimation and CLIP-Based Semantic Scoring
We propose a lightweight two-stage framework for real-time video anomaly detection. The first stage employs YOLO v11n-pose to detect persons and extract seventeen skeletal keypoints in a single forward pass. The second stage encodes each cropped person region through CLIP ViT-B/32 and computes cosine similarity against predefined textual descriptions of anomalous behaviors. This architecture eliminates the need for optical flow, standalone pose estimators, and density-based scoring modules. Experiments on CUHK Avenue, ShanghaiTech Campus, and a custom indoor dataset collected at Chulalongkorn University demonstrate an end-to-end throughput of approximately 51 FPS on an NVIDIA Titan XP GPU, a 3.36x speedup over the multi-feature baseline, while maintaining frame-level AUROC values of 89.26%, 70.26%, and 84.13%, respectively.