An infrared pedestrian detection method based on segmentation and domain adaptation learning

Benefiting from the capability of night viewing, infrared images has been widely applied to surveillance systems as an effective complement to visible-light images. However, the development of infrared pedestrian detection is still impeded by weak features and limited diversity of infrared images. Aiming at these two problems, we designed a multi-task learning framework for pedestrian detection by incorporating a semantic segmentation branch and a domain adaptation branch. Composed of UNet network with Swin Transformer, the semantic segmentation could apply spatial constraints to pedestrian detection. The domain adaptation branch aligns the features between infrared and visible-light images to improve the scene diversity. In addition, three tasks shared a basic feature extraction network to reduce computation cost. The experiment results show that the average precision (AP) of our method is superior to the EfficientDet network by 2.0% on the XDU-NIR2020 dataset and 2.2% on the CVC-09 dataset respectively.

Automated summary

This paper proposes a multi-task learning framework for improving infrared pedestrian detection by incorporating semantic segmentation and domain adaptation. The experimental results demonstrate that the proposed method outperforms other methods in terms of average precision on different datasets.