Infrared pedestrian detection often suffers from two problems, i.e., 1) the weak features of infrared images result in false alarms; 2) the generalization ability of infrared pedestrian detection methods is not satisfactory since the infrared images are similar due to the limited the acquisition method. To solve these problems, we proposed a multi-task infrared pedestrian detection method. Firstly, the domain adaptation is introduced to align the feature of visible light images and infrared images, by which visible light images are used as additional data to improve scene diversity and generalization ability. Secondly, the U-Net segmentation network is used to predict the pedestrian activity area, and the detected objects in nonpedestrian parts are filtered out to reduce the false alarm. The experiment results show that, Compared with EfficientDet, our method improved the average precision (AP) by 1.4% on the XDU-NIR2020 dataset.
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