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

Conference paper

Pal, S. and Dagiuklas, T. (2023). Benchmarking Reidentification in Multi-Camera Tracking Systems with YOLOv8 and ResNet-50. 2023 International Conference on Human-Centered Cognitive Systems (HCCS). Cardiff, UK 16 - 17 Dec 2023 IEEE. https://doi.org/10.1109/hccs59561.2023.10452624
AuthorsPal, S. and Dagiuklas, T.
TypeConference paper
Abstract

The aim of this paper is to benchmark reidentification within a multi-camera tracking system. This benchmark has been carried out 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 an 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.

KeywordsVehicle Reidentification, Multi-Camera Tracking, Object Detection, Feature Extraction, Transfer Learning, Intelligent Transportation Systems.
Year2023
PublisherIEEE
Digital Object Identifier (DOI)https://doi.org/10.1109/hccs59561.2023.10452624
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Open
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Print16 Dec 2023
Publication process dates
Deposited21 Mar 2024
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HCCS-2023-11747.pdf
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