Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection

TL;DR

This paper introduces an illumination-aware deep learning framework that leverages multispectral data and semantic segmentation to improve pedestrian detection accuracy in varying lighting conditions, especially at night.

Contribution

It proposes a novel illumination-aware weighting mechanism integrated into a multi-task deep neural network for enhanced multispectral pedestrian detection.

Findings

Outperforms state-of-the-art methods on KAIST dataset

Effectively utilizes illumination information for daytime and nighttime detection

Improves semantic segmentation accuracy for pedestrian detection

Abstract

Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g. security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to significantly boost performance of pedestrian detection. A novel illumination-aware weighting mechanism is present to accurately depict illumination condition of a scene. Such illumination information is incorporated into two-stream deep convolutional neural networks to learn multispectral human-related features under different illumination conditions (daytime and nighttime). Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy. Putting all of the pieces together, we present a powerful framework for multispectral pedestrian detection based on multi-task learning of illumination-aware pedestrian detection and semantic segmentation. Our proposed method is trained end-to-end using a well-designed multi-task loss function and outperforms state-of-the-art approaches on KAIST multispectral pedestrian dataset.