A Lightweight Fall Detection Framework for Smart-City CCTV Using Distilled Pose and Interpretable Features

Vision-based fall detection for smart-city CCTV must be fast, interpretable, and robust to nuisance alarms. In real surveillance scenes, false alarms are often caused by sitting, crouching, duplicate detections, short-lived pose noise, and brief posture changes that do not correspond to actual falls. This paper presents a lightweight CCTV fall-detection framework evaluated on URFD, Le2i, and UP-Fall. The proposed method combines teacher-guided distillation from ViTPose to a YOLO26s-pose student, custom person detection, full-body ROI extraction, nine interpretable posture features, Random Forest classification, tracking-based duplicate suppression, and post-event false-positive rejection. The distillation stage improves pose mAP50-95 from 66.8% to 70.9% and pose mAP50 from 88.9% to 91.2%. In the final stand/fall setting, Random Forest with false-positive rejection achieves 98.46% accuracy, 98.61% precision, 98.43% recall, and 98.52% F1-score. The main contribution of this work is a practical and interpretable surveillance framework that integrates distilled lightweight pose estimation, posture-based fall representation, and tracking-aware false-positive suppression for robust deployment-oriented fall detection.