Advancements in Pedestrian Re-Identification
DOI:
https://doi.org/10.62051/88dzk551Keywords:
Pedestrianv Re-ID technology; Deep learning; Global and Local Features; attention mechanism; Pooling; Loss function.Abstract
The realm of pedestrian re-identification (Re-ID) technology, a vital component in areas such as video surveillance and intelligent security, has witnessed significant advancements, particularly in the context of deep learning. This domain, despite its nascent start in the broader image processing landscape, has seen rapid evolution with the advent of robust deep learning techniques. The present article delves into a range of methodologies pertinent to pedestrian Re-ID, emphasizing both the integral loss functions in metric learning and the necessary datasets for effective Re-ID implementation. Additionally, it explores the diverse applications of pedestrian Re-ID technology, shedding light on its multifaceted utility. The discourse extends to an analysis of the impending research challenges and potential directions in this field, encapsulating the dynamic and evolving nature of pedestrian re-identification technology. In conclusion, this article offers a comprehensive overview of the current state and forward-looking insights into pedestrian Re-ID, marking a pivotal contribution to the understanding of this transformative technology.
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