Real-Time Thermal Human Activity Recognition on a CPU-Only Raspberry Pi 5 Using YOLO-Pose and Temporal Models

Deploying human pose estimation and activity recognition on low-cost, accelerator-free hardware remains challenging, particularly for thermal imaging, which supports privacy-preserving and illumination-robust monitoring but has received comparatively little attention on CPU-only edge platforms. This study presents a complete thermal activity recognition pipeline that acquires video over RTSP, estimates human pose using YOLO-pose, and classifies six activities using lightweight temporal models, evaluated entirely on a Raspberry Pi 5 without a dedicated neural-network accelerator. Using a purpose-built single-participant thermal dataset with manually annotated pose keypoints and activity labels, YOLOv8-pose, YOLO11-pose, and YOLO26-pose were benchmarked in nano and small variants across native PyTorch, ONNX, and NCNN export formats, with NCNN reducing inference time by up to 2.9× while also minimizing power consumption; the selected YOLO26n-pose/NCNN configuration achieved 92.6% mAP50–95 at approximately 86 ms per frame. Five temporal architectures (LSTM, GRU, Conv1D, Conv1D-GRU, and a compact Transformer encoder) were then systematically compared across network sizes and observation windows on held-out test recordings. Network size was found to be the dominant driver of recognition performance, and convolutional and hybrid convolutional-recurrent architectures provided the most favorable accuracy–latency trade-offs at the low-latency end, while the highest Macro F1-score of 0.879 was obtained by a GRU at a processing rate of 5.8 FPS, with processing latency below 0.7 s and window-dependent response latency of 1.2–4.7 s. These results indicate that real-time thermal activity recognition is feasible on CPU-only edge hardware through careful joint selection of export format and temporal architecture, within the single-participant, full-body-visibility setting evaluated here.