Unsupervised domain-adaptive scene-specific pedestrian detection for static video surveillance

摘要

Objects from one category may be drawn from different distributions due to diverse illuminations, backgrounds, and camera viewpoints. Traditional object detection methods generally perform poorly due to the domain shift. To address this problem, we propose to train a domain-adaptive scene-specific pedestrian detector in an unsupervised manner. A generic detector is transferred to different tar get domains from one labeled source domain dataset without human-annotated target samples. Specifically, we first extend the generic detector to a dual-boundary classifier and collect hard samples as unlabeled target samples according to the detection confidence. Then, we propose a cycle semantic transfer network to align the instance-level and class-level distributions between the source domain and target domain and automatically label the hard samples. The initial generic detector is then re-trained by these labeled hard samples and specialized to a target scene. This process can be conveniently extended to different surveillance scenarios and generate specific detectors under various static camera viewpoints. Moreover, to reduce the impact of mislabeled hard samples on the generic detector, an online gradual optimization algorithm is proposed to iteratively update the generic model, thereby obtaining an optimized process that is insensitive to individual mislabeled target samples. Extensive experiments show that even if the target domain is not manually annotated, the proposed self-learning method demonstrates the effectiveness of pedestrian detection in various domain shift scenarios, and it outperforms existing scene-specific pedestrian detection methods and some classic supervised methods.& nbsp; (c) 2021 Elsevier Ltd. All rights reserved.