# Efficient Deep Learning Models for Privacy-preserving People Counting on Low-resolution Infrared Arrays

arXiv: https://arxiv.org/abs/2304.06059 · HTML: https://mlsanity.com/paper/2304.06059/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 61 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://mlsanity.com/paper/1608.08242/) (2016)
- [Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge](https://mlsanity.com/paper/2301.10281/) (2023)
- [Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices](https://mlsanity.com/paper/2203.14907/) (2022)
- [Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition](https://mlsanity.com/paper/2203.12932/) (2022)
- [Ensemble Convolutional Neural Networks for Mode Inference in Smartphone Travel Survey](https://mlsanity.com/paper/1904.08933/) (2019)
- [Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference](https://mlsanity.com/paper/2204.10539/) (2022)
- [Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs](https://mlsanity.com/paper/2204.10541/) (2022)

## Built on by (most cited first, 4 of 4)

- [Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A Survey](https://mlsanity.com/paper/2412.14708/) (2024)
- [HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays](https://mlsanity.com/paper/2402.01226/) (2024)
- [mmCounter: Static People Counting in Dense Indoor Scenarios Using mmWave Radar](https://mlsanity.com/paper/2512.10357/) (2025)
- [MAUPITI: On-Device Prototype-Based Learning on a Smart Infrared Sensor](https://mlsanity.com/paper/2608.07192/) (2026)

## Abstract (arXiv)

Ultra-low-resolution Infrared (IR) array sensors offer a low-cost, energy-efficient, and privacy-preserving solution for people counting, with applications such as occupancy monitoring. Previous work has shown that Deep Learning (DL) can yield superior performance on this task. However, the literature was missing an extensive comparative analysis of various efficient DL architectures for IR array-based people counting, that considers not only their accuracy, but also the cost of deploying them on memory- and energy-constrained Internet of Things (IoT) edge nodes. In this work, we address this need by comparing 6 different DL architectures on a novel dataset composed of IR images collected from a commercial 8x8 array, which we made openly available. With a wide architectural exploration of each model type, we obtain a rich set of Pareto-optimal solutions, spanning cross-validated balanced accuracy scores in the 55.70-82.70% range. When deployed on a commercial Microcontroller (MCU) by STMicroelectronics, the STM32L4A6ZG, these models occupy 0.41-9.28kB of memory, and require 1.10-7.74ms per inference, while consuming 17.18-120.43 μJ of energy. Our models are significantly more accurate than a previous deterministic method (up to +39.9%), while being up to 3.53x faster and more energy efficient. Further, our models' accuracy is comparable to state-of-the-art DL solutions on similar resolution sensors, despite a much lower complexity. All our models enable continuous, real-time inference on a MCU-based IoT node, with years of autonomous operation without battery recharging.