Edge-AI Enabled Real-Time Multi-Camera Casualty Detection and Tracking for Military IoT

Abstract

The advancement of military internet of things (IoT) surveillance demands real-time casualty detection across distributed camera networks under diverse environmental conditions. Traditional surveillance systems suffer from high latency, bandwidth inefficiency and unreliable cross-camera identity tracking, indicating the need for advanced detection and tracking systems. This study presents an edge-based multicamera casualty detection and tracking system for military IoT networks. The proposed framework, called CCTV-TrackNet, integrates lightweight AI on distributed camera nodes built on Raspberry Pi Zero2W hardware with high-resolution cameras and sensors such as GPS and IMU. At each node, YOLOv12n performs real-time person and casualty detection, DeepSORT maintains short-term continuity, and OSNet-based Re-ID extracts appearance embeddings for cross-camera association. A central server aggregates metadata for global identification, visualizes trajectories, and generates real-time alerts. Experimental results show 90.77% detection accuracy, 37% and 36% reduction in false cases, 30.3 FPS edge performance, and 84.20% cross-camera ID consistency.