Video-Based Person Re-Identification: Methods, Datasets, and Deep Learning
Manisha Talware1, Sanjay Koli2
1Manisha Talware, Research Scholar at G.H. Raisoni College of Engineering and Management, Pune, India.
2Dr Sanjay Koli, Professor, at G.H. Raisoni College of Engineering and Management, Pune, India.
Manuscript received on January 21, 2020. | Revised Manuscript received on February 25, 2020. | Manuscript published on February 29, 2020. | PP: 4249-4254 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6524029320/2020©BEIESP | DOI: 10.35940/ijeat.C6524.0293200
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Person Re-Identification, Camera Network, Video Analytics, Deep Learning, pedestrian detection.