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 language | English |
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Title of host publication | Proceedings - 2023 Human-Centered Cognitive Systems, HCCS 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350359183 |
DOIs | |
Publication status | Published - 16 Dec 2023 |
Event | 2023 International Conference on Human-Centered Cognitive Systems (HCCS) - Duration: 16 Dec 2023 → … |
Publication series
Name | Proceedings - 2023 Human-Centered Cognitive Systems, HCCS 2023 |
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Conference
Conference | 2023 International Conference on Human-Centered Cognitive Systems (HCCS) |
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Period | 16/12/23 → … |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- Feature Extraction
- Intelligent Transportation Systems
- Multi-Camera Tracking
- Object Detection
- Transfer Learning
- Vehicle Reidentification