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Abstract
This paper introduces a Smart City solution designed to run on edge devices, leveraging NVIDIA's DeepStream SDK for efficient urban surveillance. We evaluate five object-tracking approaches, using YOLO as the baseline detector and integrating three Nvidia DeepStream trackers: IOU, NvSORT, and NvDCF. Additionally, we propose a custom tracker based on Optical Flow and Kalman filtering. The presented approach combines advanced machine learning and deep learning techniques to enhance object tracking in intelligent traffic management systems, contributing to the evolving landscape of urbanization. Experimental results highlight the challenges and potential improvements in tracking accuracy, particularly in addressing object misclassification. In the conducted study, the proposed method achieved average precision = 0.95.
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Authors (8)
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Jo Kang-Hyun
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Miłosz Kaszyński
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Tomasz Ludwisiak
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Kocejko T., Neumann T., Mazur-Milecka M., Kowalczyk N., Rumiński J., Kang-Hyun J., Kaszyński M., Ludwisiak T.: Evaluating the Use of Edge Devices for Detection and Tracking of Vehicles in Smart City Environment// / : , 2024,
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/iwis62722.2024.10706028
- Sources of funding:
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- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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