Open AccessVideo Based Person Re Identification Methods, Datasets, and Deep Learning
Author(s) -
Manisha Talware,
S. M. Koli
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c6524.029320
Subject(s) - computer science , artificial intelligence , identification (biology) , discriminative model , deep learning , analytics , feature learning , matching (statistics) , machine learning , metric (unit) , feature (linguistics) , focus (optics) , data science , linguistics , statistics , botany , operations management , mathematics , philosophy , physics , optics , economics , biology
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.