# Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras

arXiv: https://arxiv.org/abs/2209.11335 · HTML: https://mlsanity.com/paper/2209.11335/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images](https://mlsanity.com/paper/1804.04488/) (2018)
- [Learning to Anonymize Faces for Privacy Preserving Action Detection](https://mlsanity.com/paper/1803.11556/) (2018)
- [Privacy-Preserving Human Activity Recognition from Extreme Low Resolution](https://mlsanity.com/paper/1604.03196/) (2016)
- [Studying Very Low Resolution Recognition Using Deep Networks](https://mlsanity.com/paper/1601.04153/) (2016)
- [Combinational Class Activation Maps for Weakly Supervised Object Localization](https://mlsanity.com/paper/1910.05518/) (2019)
- [One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks](https://mlsanity.com/paper/2011.10772/) (2020)
- [Semi-Coupled Two-Stream Fusion ConvNets for Action Recognition at Extremely Low Resolutions](https://mlsanity.com/paper/1610.03898/) (2016)
- [Home Activity Monitoring using Low Resolution Infrared Sensor](https://mlsanity.com/paper/1811.05416/) (2018)
- [Privacy-preserving Object Detection](https://mlsanity.com/paper/2103.06587/) (2021)

## Built on by (most cited first, 7 of 7)

- [Visual Modality Prompt for Adapting Vision-Language Object Detectors](https://mlsanity.com/paper/2412.00622/) (2024)
- [MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection](https://mlsanity.com/paper/2404.18849/) (2024)
- [Modality Translation for Object Detection Adaptation Without Forgetting Prior Knowledge](https://mlsanity.com/paper/2404.01492/) (2024)
- [MMOne: Representing Multiple Modalities in One Scene](https://mlsanity.com/paper/2507.11129/) (2025)
- [WiSE-OD: Benchmarking Robustness in Infrared Object Detection](https://mlsanity.com/paper/2507.18925/) (2025)
- [Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting](https://mlsanity.com/paper/2311.11974/) (2023)
- [Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors](https://mlsanity.com/paper/2411.11151/) (2024)

## Abstract (arXiv)

In intelligent building management, knowing the number of people and their location in a room are important for better control of its illumination, ventilation, and heating with reduced costs and improved comfort. This is typically achieved by detecting people using compact embedded devices that are installed on the room's ceiling, and that integrate low-resolution infrared camera, which conceals each person's identity. However, for accurate detection, state-of-the-art deep learning models still require supervised training using a large annotated dataset of images. In this paper, we investigate cost-effective methods that are suitable for person detection based on low-resolution infrared images. Results indicate that for such images, we can reduce the amount of supervision and computation, while still achieving a high level of detection accuracy. Going from single-shot detectors that require bounding box annotations of each person in an image, to auto-encoders that only rely on unlabelled images that do not contain people, allows for considerable savings in terms of annotation costs, and for models with lower computational costs. We validate these experimental findings on two challenging top-view datasets with low-resolution infrared images.