Learning Cross-Modal Deep Representations for Robust Pedestrian Detection

TL;DR

This paper introduces a cross-modal deep learning framework that leverages thermal data during training to improve pedestrian detection in poor lighting, achieving superior results on challenging datasets.

Contribution

A novel cross-modality learning approach that transfers thermal data features to RGB-based pedestrian detection, enhancing robustness without requiring thermal data at test time.

Findings

Outperforms state-of-the-art on KAIST dataset

Competitive results on Caltech dataset

Effective learning of illumination-robust features

Abstract

This paper presents a novel method for detecting pedestrians under adverse illumination conditions. Our approach relies on a novel cross-modality learning framework and it is based on two main phases. First, given a multimodal dataset, a deep convolutional network is employed to learn a non-linear mapping, modeling the relations between RGB and thermal data. Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results. In this way, features which are both discriminative and robust to bad illumination conditions are learned. Importantly, at test time, only the second pipeline is considered and no thermal data are required. Our extensive evaluation demonstrates that the proposed approach outperforms the state-of- the-art on the challenging KAIST multispectral pedestrian dataset and it is competitive with previous methods on the popular Caltech dataset.