# Bright Channel Prior Attention for Multispectral Pedestrian Detection

arXiv: https://arxiv.org/abs/2305.12845 · HTML: https://mlsanity.com/paper/2305.12845/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 7 of 7)

- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems](https://mlsanity.com/paper/2008.03043/) (2020)
- [Multispectral Deep Neural Networks for Pedestrian Detection](https://mlsanity.com/paper/1611.02644/) (2016)
- [From Handcrafted to Deep Features for Pedestrian Detection: A Survey](https://mlsanity.com/paper/2010.00456/) (2020)
- [GFD-SSD: Gated Fusion Double SSD for Multispectral Pedestrian Detection](https://mlsanity.com/paper/1903.06999/) (2019)
- [Towards Generalizable Person Re-identification with a Bi-stream Generative Model](https://mlsanity.com/paper/2206.09362/) (2022)
- [Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain Adaptation](https://mlsanity.com/paper/2202.09750/) (2022)

## Built on by (most cited first, 1 of 1)

- [Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey](https://mlsanity.com/paper/2401.07801/) (2024)

## Abstract (arXiv)

Multispectral methods have gained considerable attention due to their promising performance across various fields. However, most existing methods cannot effectively utilize information from two modalities while optimizing time efficiency. These methods often prioritize accuracy or time efficiency, leaving room for improvement in their performance. To this end, we propose a new method bright channel prior attention for enhancing pedestrian detection in low-light conditions by integrating image enhancement and detection within a unified framework. The method uses the V-channel of the HSV image of the thermal image as an attention map to trigger the unsupervised auto-encoder for visible light images, which gradually emphasizes pedestrian features across layers. Moreover, we utilize unsupervised bright channel prior algorithms to address light compensation in low light images. The proposed method includes a self-attention enhancement module and a detection module, which work together to improve object detection. An initial illumination map is estimated using the BCP, guiding the learning of the self-attention map from the enhancement network to obtain more informative representation focused on pedestrians. The extensive experiments show effectiveness of the proposed method is demonstrated through.