# Efficient Thermal Pose Estimation: Balancing Accuracy and Edge Deployment for Smart Home Activity Recognition

## TL;DR

This paper explores efficient thermal-image pose estimation for smart homes, finding that FP16 precision balances accuracy and performance on edge devices.

## Contribution

The study introduces a thermal dataset and evaluates model scales and precision formats for edge deployment in activity recognition.

## Key findings

- TensorRT FP16 maintains pose accuracy while reducing latency and power consumption compared to FP32.
- INT8 further reduces power but causes accuracy losses in some configurations.
- FP16 is recommended for the best accuracy-efficiency balance on edge devices.

## Abstract

This study investigates efficient thermal-image human pose estimation under edge deployment constraints for smart home activity recognition. A single-person thermal dataset of 2500 images was collected and annotated with 17 body keypoints. YOLO11-pose and YOLOv8-pose models were trained and evaluated across all five model scales (n–x) at three input resolutions 640 × 512, 320 × 256, and 160 × 128 px. The accuracy was evaluated using box mean Average Precision (mAP50–95), pose mAP50–95, and Object Keypoint Similarity (OKS) metrics. Runtime performance was assessed using per-image latency and power measurements on three NVIDIA Jetson platforms: Orin Nano 4 GB, Orin Nano 8 GB and AGX Orin 64 GB, using PyTorch and TensorRT at FP32, FP16, INT8 precision. Human detection remained consistently high across model variants, whereas pose accuracy decreased as the input resolution was reduced. TensorRT FP16 preserved pose accuracy relative to PyTorch and TensorRT FP32, with minimal changes in OKS and pose mAP50–95, while notably reducing per-image latency and power consumption. INT8 further reduced power consumption and in some configurations improved latency, but caused configuration-dependent losses in OKS and pose mAP50–95. The findings indicate that FP16 offers the best accuracy–efficiency balance for thermal pose estimation on edge devices, while practical feasibility depends on device capabilities and memory limitations.