Multi-Source Domain Fusion Cross-Domain Pedestrian Recognition Based on High-Quality Intermediate Domains

Abstract

Pedestrian detection has received considerable attention over the last few years because it can be combined with pedestrian tracking and re-identification in areas such as vehicle-assisted driving and intelligent video surveillance. Although existing pedestrian detection techniques have achieved excellent results, problems such as domain gaps lead to poor generalization performance in these techniques, thereby limiting its application and practical value. This study proposed a high-quality integration domain framework for pedestrian recognition. First, the source domains are produced as super-resolution training data. The HCycleGAN model uses super-resolution algorithms and a generative framework to generate high-quality intermediate domains. Second, a multi-source domain fusion scheme based on the NPIQE module is proposed to improve the generated framework’s quality and reduce overfitting of the dataset. It fuses images in three aspects: similarity, blurriness and unsupervised image quality score values. Finally, we use an anchor-free center and scale prediction model for pedestrian detection. The experimental dataset contained two common pedestrian detection datasets, Caltech and CityPersons. Cross-domain experimental results show that the framework can reduce cross-domain detection miss rate from CityPersons to Caltech by 6.3% and from Caltech to CityPersons by 4.4%. The training of CityPersons in Caltech achieves almost the same detection accuracy as that of the Caltech original domain. In conclusion, the framework presented in this study is effective for cross-domain pedestrian detection and can provide ideas and inspiration for future practical applications.

Keywords

• Domain adaptation
• object detector
• transfer learning
• deep learning