Deep Learning for Video-based Person Re-Identification: A Survey
- URL: http://arxiv.org/abs/2303.11332v2
- Date: Tue, 17 Oct 2023 06:10:14 GMT
- Title: Deep Learning for Video-based Person Re-Identification: A Survey
- Authors: Khawar Islam
- Abstract summary: This paper introduces a review of up-to-date advancements in deep learning approaches for video re-ID.
It covers brief video re-ID methods with their limitations, major milestones with technical challenges, and architectural design.
- Score: 1.6317061277457001
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Video-based person re-identification (video re-ID) has lately fascinated
growing attention due to its broad practical applications in various areas,
such as surveillance, smart city, and public safety. Nevertheless, video re-ID
is quite difficult and is an ongoing stage due to numerous uncertain challenges
such as viewpoint, occlusion, pose variation, and uncertain video sequence,
etc. In the last couple of years, deep learning on video re-ID has continuously
achieved surprising results on public datasets, with various approaches being
developed to handle diverse problems in video re-ID. Compared to image-based
re-ID, video re-ID is much more challenging and complex. To encourage future
research and challenges, this first comprehensive paper introduces a review of
up-to-date advancements in deep learning approaches for video re-ID. It broadly
covers three important aspects, including brief video re-ID methods with their
limitations, major milestones with technical challenges, and architectural
design. It offers comparative performance analysis on various available
datasets, guidance to improve video re-ID with valuable thoughts, and exciting
research directions.
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