通过骨骼分析实时识别监控中推搡行为，助力城市安全预警。
Human Activity Recognition Method for Moderate Violence Detection
- 用YOLO11和姿态模型提取人体关键点，计算身体倾斜与关节角度。
- 在真实复杂场景下仍保持72%精确率，前端视角精度达98%。
- 适合安防系统早期暴力干预，尤其适用于公共空间监控。
公共场所的肢体暴力是重大公共健康问题，轻微推搡常为严重冲突的前兆。本文提出一种实时检测监控视频中推搡行为的自动化系统。方法结合YOLO11与YOLO11-Pose进行人体检测与骨骼关键点提取，通过计算肩髋间关节角度与身体倾斜度，训练随机森林分类器区分正常行为与攻击性接触。系统在三种逐步增加难度的案例中评估：在理想正面视角的受控环境中，模型精度达0.98；在真实监控设备拍摄的高角度、陡峭视角复杂场景下，尽管存在明显透视畸变与视觉噪声，系统仍保持0.72的精度。结果表明，基于骨骼分析的早期暴力识别在城市安防中具有可行性。
原文摘要 · Abstract (English)
Physical violence in public spaces is a significant public health concern, with minor incidents such as pushing often serving as precursors to more severe escalations. This research develops an automated system for the real-time detection of moderate physical violence, specifically pushing, in surveillance camera footage. The proposed solution integrates state-of-the-art computer vision models, utilizing YOLO11 and YOLO11-Pose for human detection and skeletal keypoint extraction. By calculating body inclination and joint angles between shoulders and hips, a Random Forest classifier was trained to distinguish between normal behavior and aggressive physical contact. The system's performance was evaluated through three progressive case studies representing increasing levels of difficulty. In controlled environments with frontal views, the model achieved a precision of 0.98. In the most challenging scenario, featuring high-altitude, steep-angle recordings from real-world surveillance infrastructure, the system maintained a precision of 0.72 despite significant perspective distortion and visual noise. These results demonstrate the feasibility of using skeletal analysis for early violence intervention in urban security contexts.
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