Tackling Domain Shifts in Person Re-Identification: A Survey and Analysis

Vuong D. Nguyen, Samiha Mirza, Abdollah Zakeri, Ayush Gupta, Khadija Khaldi, Rahma Aloui, Pranav Mantini, Shishir K. Shah, Fatima Merchant; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4149-4159

The necessity for a Person ReID system for rapidly evolving urban surveillance applications is severely challenged by domain shifts-variations in data distribution that occur across different environments or times. In this paper we provide the first empirical review of domain shift in person ReID which includes three settings namely Unsupervised Domain Adaptation ReID Domain Generalizable ReID and Lifelong ReID. We observe that existing approaches only tackle domain shifts caused by cross-dataset setting while ignoring intra-dataset attribute domain shifts caused by changes in clothing shape or gait which is very common in ReID. Thus we enhance research directions in this field by redefining domain shift in ReID as the combination of attribute domain shift with cross-dataset domain shift. With a focus on Lifelong Re-ID methods we conduct an extensive comparison on a fair experimental setup and provide an in-depth analysis of these methods under both non-cloth-changing and cloth-changing Re-ID scenarios. Insights into the strengths and limitations of these methods based on their performance are studied. This paper outlines future research directions and paves the way for the development of more adaptive resilient and enduring cross-domain ReID systems. Code is available at https://github.com/dustin-nguyen-qil/Lifelong-CC-ReID.