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Publication . Article . 2020

Video Based Person Re Identification Methods, Datasets, and Deep Learning

Manisha Talware; Sanjay Koli;
Open Access
Published: 28 Feb 2020 Journal: International Journal of Engineering and Advanced Technology, volume 9, pages 4,249-4,254 (eissn: 2249-8958, Copyright policy )
Publisher: Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Abstract
Video Analytics applications like security and surveillance face a critical problem of person re-identification abbreviated as re-ID. The last decade witnessed the emergence of large-scale datasets and deep learning methods to use these huge data volumes. Most current re-ID methods are classified into either image-based or video-based re-ID. Matching persons across multiple camera views have attracted lots of recent research attention. Feature representation and metric learning are major issues for person re-identification. The focus of re-ID work is now shifting towards developing end-to-end re-Id and tracking systems for practical use with dynamic datasets. Most previous works contributed to the significant progress of person re-identification on still images using image retrieval models. This survey considers the more informative and challenging video-based person re-ID problem, pedestrian re-ID in particular. Publicly available datasets and codes are listed as a part of this work. Current trends which include open re-identification systems, use of discriminative features and deep learning is marching towards new applications in security and surveillance, typically for tracking.
Subjects by Vocabulary

Microsoft Academic Graph classification: Re identification Machine learning computer.software_genre computer Artificial intelligence business.industry business Computer science Deep learning Video based

Subjects

Computer Science Applications, General Engineering, Environmental Engineering

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