# Pedestrian Detection in Thermal Images using Saliency Maps

arXiv: https://arxiv.org/abs/1904.06859 · HTML: https://mlsanity.com/paper/1904.06859/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

## Built on by (most cited first, 15 of 15)

- [Fusion-Mamba for Cross-modality Object Detection](https://mlsanity.com/paper/2404.09146/) (2024)
- [Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving](https://mlsanity.com/paper/2105.12713/) (2021)
- [SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving](https://mlsanity.com/paper/2103.03150/) (2021)
- [Object Detection in Thermal Spectrum for Advanced Driver-Assistance Systems (ADAS)](https://mlsanity.com/paper/2109.09854/) (2021)
- [Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey](https://mlsanity.com/paper/2401.07801/) (2024)
- [Robust pedestrian detection in thermal imagery using synthesized images](https://mlsanity.com/paper/2102.02005/) (2021)
- [Feature Calibration Network for Occluded Pedestrian Detection](https://mlsanity.com/paper/2212.05717/) (2022)
- [Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion](https://mlsanity.com/paper/2307.03623/) (2023)
- [Mitigating Bias Using Model-Agnostic Data Attribution](https://mlsanity.com/paper/2405.05031/) (2024)
- [Partially fake it till you make it: mixing real and fake thermal images for improved object detection](https://mlsanity.com/paper/2106.13603/) (2021)
- [Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection](https://mlsanity.com/paper/2309.16592/) (2023)
- [ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions](https://mlsanity.com/paper/2308.10161/) (2023)
- [Data Generation Scheme for Thermal Modality with Edge-Guided Adversarial Conditional Diffusion Model](https://mlsanity.com/paper/2408.03748/) (2024)
- [Low-light Pedestrian Detection in Visible and Infrared Image Feeds: Issues and Challenges](https://mlsanity.com/paper/2311.08557/) (2023)
- [Automatic Labelling for Low-Light Pedestrian Detection](https://mlsanity.com/paper/2507.02513/) (2025)

## Abstract (arXiv)

Thermal images are mainly used to detect the presence of people at night or in bad lighting conditions, but perform poorly at daytime. To solve this problem, most state-of-the-art techniques employ a fusion network that uses features from paired thermal and color images. Instead, we propose to augment thermal images with their saliency maps, to serve as an attention mechanism for the pedestrian detector especially during daytime. We investigate how such an approach results in improved performance for pedestrian detection using only thermal images, eliminating the need for paired color images. For our experiments, we train the Faster R-CNN for pedestrian detection and report the added effect of saliency maps generated using static and deep methods (PiCA-Net and R3-Net). Our best performing model results in an absolute reduction of miss rate by 13.4% and 19.4% over the baseline in day and night images respectively. We also annotate and release pixel level masks of pedestrians on a subset of the KAIST Multispectral Pedestrian Detection dataset, which is a first publicly available dataset for salient pedestrian detection.