Domain-Adaptive Pedestrian Detection in Thermal Images

This paper presents an approach to pedestrian detection in thermal infrared (thermal) images with limited annotations. The key idea is to adapt the abundance of color images associated with bounding box annotations to the thermal domain for training the pedestrian detector. To this end, we couple a domain adaptation component that consists of a pair of image transformers with a pedestrian detector in the thermal domain and train the entire network end-to-end. The image transformers act as a data augmentation tool that progressively improves synthetic examples on the fly for training the pedestrian detector. To aid the training process, we introduce a detection loss defined on both real thermal images and synthetic thermal images transformed from the color domain. The proposed detector outperforms existing methods on the thermal images from the KAIST detection benchmark [1].