Person Re-Identification using Deep Learning Networks: A Systematic Review

• URL: http://arxiv.org/abs/2012.13318v1
• Date: Thu, 24 Dec 2020 16:36:59 GMT
• Title: Person Re-Identification using Deep Learning Networks: A Systematic Review
• Authors: Ankit Yadav, Dinesh Kumar Vishwakarma
• Abstract summary: Person re-identification has received a lot of attention from the research community in recent times. Person re-identification lies at the heart of research relevant to tracking robberies, preventing terrorist attacks and other security critical events. This review deals with the latest state-of-the-art deep learning based approaches for person re-identification.
• Score: 8.452237741722726
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Person re-identification has received a lot of attention from the research community in recent times. Due to its vital role in security based applications, person re-identification lies at the heart of research relevant to tracking robberies, preventing terrorist attacks and other security critical events. While the last decade has seen tremendous growth in re-id approaches, very little review literature exists to comprehend and summarize this progress. This review deals with the latest state-of-the-art deep learning based approaches for person re-identification. While the few existing re-id review works have analysed re-id techniques from a singular aspect, this review evaluates numerous re-id techniques from multiple deep learning aspects such as deep architecture types, common Re-Id challenges (variation in pose, lightning, view, scale, partial or complete occlusion, background clutter), multi-modal Re-Id, cross-domain Re-Id challenges, metric learning approaches and video Re-Id contributions. This review also includes several re-id benchmarks collected over the years, describing their characteristics, specifications and top re-id results obtained on them. The inclusion of the latest deep re-id works makes this a significant contribution to the re-id literature. Lastly, the conclusion and future directions are included.

Related papers

• Keypoint Promptable Re-Identification [76.31113049256375]
  Occluded Person Re-Identification (ReID) is a metric learning task that involves matching occluded individuals based on their appearance. We introduce Keypoint Promptable ReID (KPR), a novel formulation of the ReID problem that explicitly complements the input bounding box with a set of semantic keypoints. We release custom keypoint labels for four popular ReID benchmarks. Experiments on person retrieval, but also on pose tracking, demonstrate that our method systematically surpasses previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-07-25T15:20:58Z)
• A Comprehensive Review of Few-shot Action Recognition [64.47305887411275]
  Few-shot action recognition aims to address the high cost and impracticality of manually labeling complex and variable video data. It requires accurately classifying human actions in videos using only a few labeled examples per class.
  arXiv  Detail & Related papers  (2024-07-20T03:53:32Z)
• Transformer for Object Re-Identification: A Survey [69.61542572894263]
  Vision Transformers have spurred a growing number of studies delving deeper into Transformer-based Re-ID. This paper provides a comprehensive review and in-depth analysis of the Transformer-based Re-ID. Considering the trending unsupervised Re-ID, we propose a new Transformer baseline, UntransReID, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-01-13T03:17:57Z)
• Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives [8.026271369888956]
  Occluded person Re-ID refers to a pedestrian matching method that deals with challenges such as pedestrian information loss, noise interference, and perspective misalignment. We scientifically classify and analyze existing deep learning-based occluded person Re-ID methods from various perspectives, summarizing them concisely.
  arXiv  Detail & Related papers  (2023-11-01T15:52:51Z)
• Deep Learning for Video-based Person Re-Identification: A Survey [1.6317061277457001]
  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.
  arXiv  Detail & Related papers  (2023-03-21T05:50:53Z)
• Deep Learning-based Occluded Person Re-identification: A Survey [19.76711535866007]
  Occluded person re-identification (Re-ID) aims at addressing the occlusion problem when retrieving the person of interest across multiple cameras. This paper provides a systematic survey of occluded person Re-ID methods. We summarize four issues caused by occlusion in person Re-ID, i.e., position misalignment, scale misalignment, noisy information, and missing information.
  arXiv  Detail & Related papers  (2022-07-29T03:10:18Z)
• Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends [2.4907242954727926]
  Person re-identification (Re-ID) is one of the primary components of an automated visual surveillance system. It aims to automatically identify/search persons in a multi-camera network having non-overlapping field-of-views.
  arXiv  Detail & Related papers  (2022-02-26T11:55:57Z)
• Deep Long-Tailed Learning: A Survey [163.16874896812885]
  Deep long-tailed learning aims to train well-performing deep models from a large number of images that follow a long-tailed class distribution. Long-tailed class imbalance is a common problem in practical visual recognition tasks. This paper provides a comprehensive survey on recent advances in deep long-tailed learning.
  arXiv  Detail & Related papers  (2021-10-09T15:25:22Z)
• Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions [64.68497473454816]
  Unsupervised person Re-ID has drawn increasing attention for its potential to address the scalability issue in person Re-ID. Unsupervised person Re-ID is challenging primarily due to lacking identity labels to supervise person feature learning. This survey review recent works on unsupervised person Re-ID from the perspective of challenges and solutions.
  arXiv  Detail & Related papers  (2021-09-01T00:01:35Z)
• Deep Learning for Person Re-identification: A Survey and Outlook [233.36948173686602]
  Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings.
  arXiv  Detail & Related papers  (2020-01-13T12:49:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.