Skeleton-based Anomaly Detection for CCTV Surveillance: A Real-Time Framework with Emergency Escalation
Conventional video surveillance based on pixel-level deep-learning models is resource hungry, processes gigabytes of video material, and retains biometric identifying data. This paper describes a lightweight, privacy-sensitive alternative that uses skeletal pose estimation to replace pixel-based processing. We only process 33 coordinates for body joints using MediaPipe BlazePose, compressing the data by 2000× and discarding all visual identity data at the outset. The innovation is a hybrid dual- layer classifier that combines a Random Forest classifier trained on spatiotemporal features and deterministic rules from physics (velocity thresholding and kinematic plausibility), these rules help handle ambiguous cases when the Random Forest classifier is uncertain. Testing on 1145 frames of normal, suspicious, and dangerous activities achieves an overall accuracy of 87.42% with 0.52 precision and 0.89 recall for dangerous activities. The system has under 15 ms inference time on standard laptop CPUs and under 35 ms on Raspberry Pi 4, supporting real-time edge computing without GPUs. The framework reduces computation compared to CNN-based approaches and can run on $50 edge devices. Experiments show that skeletal geometric properties are sufficient for behavior classification without relying on appearance-based biometric features.