Fine-grained Unsupervised Domain Adaptation for Gait Recognition.

Gait recognition has emerged as a promising technique for the long-range retrieval of pedestrians, providing numerous advantages such as accurate identification in challenging conditions and non-intrusiveness, making it highly desirable for improving public safety and security. However, the high cost of labeling datasets, which is a prerequisite for most existing fully supervised approaches, poses a significant obstacle to the development of gait recognition. Recently, some unsupervised methods for gait recognition have shown promising results. However, these methods mainly rely on a fine-tuning approach that does not sufficiently consider the relationship between source and target domains, leading to the catastrophic forgetting of source domain knowledge. This paper presents a novel perspective that adjacent-view sequences exhibit overlapping views, which can be leveraged by the network to gradually attain cross-view and cross-dressing capabilities without pre-training on the labeled source domain. Specifically, we propose a fine-grained Unsupervised Domain Adaptation (UDA) framework that iteratively alternates between two stages. The initial stage involves offline clustering, which transfers knowledge from the labeled source domain to the unlabeled target domain and adaptively generates pseudo-labels according to the expressiveness of each part. Subsequently, the second stage encompasses online training, which further achieves cross-dressing capabilities by continuously learning to distinguish numerous features of source and target domains. The effectiveness of the proposed method is demonstrated through extensive experiments conducted on widely-used public gait datasets.