Approaches to Person Re-identification: Progress and Challenges

Dear Colleagues,

In the dynamic field of digital imaging and surveillance, person re-identification (Re-ID) has emerged as a pivotal area of research due to its critical application in scenarios ranging from security systems to customer behavior analysis. The task involves recognizing individuals across different scenes or time instances, despite the complexities introduced by changing viewpoints, lighting conditions, occlusions, varying human activities and changes in clothing. This presents both significant challenges and opportunities for advancing imaging technology.

This Special Issue is dedicated to highlighting recent advancements and encouraging the development of innovative approaches in person Re-ID. It aims to showcase novel methodologies and explore practical applications that push the boundaries of digital imaging systems. Our objective is to curate a collection of at least 10 high-quality articles that tackle these pressing challenges and provide new insights into person Re-ID technologies. The successful achievement of this goal could potentially lead to the publication of these works in book form.
We welcome submissions of original research articles and comprehensive reviews that explore, but are not limited to, the following areas:

• The potential of self-supervised learning techniques to enhance person Re-ID;
• The integration of textual information with visual data, investigating how descriptions and annotations can complement visual cues to boost the accuracy and robustness of Re-ID systems;
• Adapting and innovating with pre-trained large vision models such as CLIP and DINO, tailoring them to meet the specific challenges of person Re-ID, focusing on feature transferability and fine-tuning;
• Address the cross-dataset domain challenges by developing strategies that improve model robustness and generalization;
• Broaden the scope of person re-identification to encompass a wider range of human activities, such as sporting events, to enhance the utility and applicability of Re-ID technologies in dynamic scenarios;
• Tackle the complexities of cloth-changing scenarios in person Re-ID.

We look forward to your contributions. Whether your work is theoretical or empirical, it will significantly contribute to our collective understanding of the challenges and advancements in person Re-ID.

Dr. Feng Liu
Guest Editor

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• person re-identification
• self-supervised learning
• multimodal data
• integration pre-trained vision models
• cross-dataset generalization
• diverse human activity
• clothing variation in re-ID
• feature transferability
• surveillance technologies
• image and text fusion