Guided Attentive Feature Fusion for Multispectral Pedestrian Detection

Abstract

Multispectral image pairs can provide complementary visual information, making pedestrian detection systems more robust and reliable. To benefit from both RGB and thermal IR modalities, we introduce a novel attentive multispectral feature fusion approach. Under the guidance of the inter- and intra-modality attention modules, our deep learning architecture learns to dynamically weigh and fuse the multispectral features. Experiments on two public multi-spectral object detection datasets demonstrate that the proposed approach significantly improves the detection accuracy at a low computation cost.