Exploring Frontier Technologies in Video-Based Person Re-Identification: A Survey on Deep Learning Approach

Video-based person re-identification (Re-ID), a subset of retrieval tasks, faces challenges like uncoordinated advancements in deep learning have significantly improved video-based person Re-ID, laying a solid foundation for further progress in the field. In order to enrich researchers' insights into the latest research findings and prospective developments, we offer an extensive overview and meticulous analysis of contemporary video-based person ReID methodologies, with a specific emphasis on network architecture design and loss function design. Firstly, we introduce methods based on network architecture design and loss function design from multiple perspectives, and analyzes the advantages and disadvantages of these methods. Furthermore, we provide a synthesis of prevalent datasets and key evaluation metrics utilized within this field to assist researchers in assessing methodological efficacy and establishing benchmarks for performance evaluation. Lastly, through a critical evaluation of the experimental outcomes derived from various methodologies across four prominent public datasets, we identify promising research avenues and offer valuable insights to steer future exploration and innovation in this vibrant and evolving field of video-based person Re-ID. This comprehensive analysis aims to equip researchers with the necessary knowledge and strategic foresight to navigate the complexities of video-based person Re-ID, fostering continued progress and breakthroughs in this challenging yet promising research domain.