This is the official repository of the paper: Efficient People Counting in Thermal Images: The Benchmark of Resource-Constrained Hardware.

The repository includes the code for reproducing experiments and obtaining results from the aforementioned publication. This repository is organized as follows:

• thermo directory contains code and scripts related to model training, optimization, and quantization phases,
• evaluation folder consists of hardware deployment, inference, and evaluation tools.

• Citation
• Dataset
• Benchmark Hardware
• Neural Network Architecture
• Results

@ARTICLE{9964383,
  author={Piechocki, Mateusz and Kraft, Marek and Pajchrowski, Tomasz and Aszkowski, Przemyslaw and Pieczynski, Dominik},
  journal={IEEE Access}, 
  title={Efficient People Counting in Thermal Images: The Benchmark of Resource-Constrained Hardware}, 
  year={2022},
  volume={10},
  number={},
  pages={124835-124847},
  doi={10.1109/ACCESS.2022.3225233}
}

The benchmark utilizes Thermo Presence dataset that consists of 13 644 low-resolution thermal images recorded in office spaces. The dataset distribution is shown in the table below. The training, validation, and test split was chosen according to the authors' choice which is described in their project repository.

The neural network is based on U-Net architecture with shallow structure, single-channel input, and output. The proposed model has only 46 577 parameters.

The figure above consists of current consumption characteristics measured on benchmarked hardware. The graphs are limited to 180 seconds in order to highlight peaks related to the prediction stage.