Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
This addresses robust pedestrian detection for autonomous systems in low-light conditions, representing an incremental improvement over existing methods.
The paper tackles pedestrian detection under adverse illumination by learning cross-modal deep representations from RGB and thermal data, achieving state-of-the-art performance on the KAIST dataset and competitive results on Caltech.
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.