Benchmarking Reidentification in Multi-Camera Tracking Systems with YOLOv8 and ResNet-50

Shaurya Pal, Tasos Dagiuklas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The primary aim of this paper is to benchmark reidentification within a multi-camera tracking system. This benchmark has been developed by leveraging transfer learning, utilizing YOLOv8 for real-time object detection and ResNet-50 for feature extraction. The objective is to evaluate the system's performance in accurately reidentifying vehicles across multiple cameras in real-world traffic surveillance scenarios. This benchmarking endeavor aims to provide a standardized evaluation framework for assessing the capabilities and limitations of vehicle reidentification techniques, with a focus on their applicability in challenging conditions such as low-light environments, image compression, and object occlusions.

Original languageEnglish
Title of host publicationProceedings - 2023 Human-Centered Cognitive Systems, HCCS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350359183
DOIs
Publication statusPublished - 16 Dec 2023
Event2023 International Conference on Human-Centered Cognitive Systems (HCCS) -
Duration: 16 Dec 2023 → …

Publication series

NameProceedings - 2023 Human-Centered Cognitive Systems, HCCS 2023

Conference

Conference2023 International Conference on Human-Centered Cognitive Systems (HCCS)
Period16/12/23 → …

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Feature Extraction
  • Intelligent Transportation Systems
  • Multi-Camera Tracking
  • Object Detection
  • Transfer Learning
  • Vehicle Reidentification

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