Smart City Gnosys

Smart city article details

Title	Evaluating The Use Of Edge Devices For Detection And Tracking Of Vehicles In Smart City Environment
ID_Doc	24693
Authors	Kocejko T.; Neumann T.; Mazur-Milecka M.; Kowalczyk N.; Ruminski J.; Jo K.-H.; Kaszynski M.; Ludwisiak T.
Year	2024
Published	2024 International Workshop on Intelligent Systems, IWIS 2024
DOI	http://dx.doi.org/10.1109/IWIS62722.2024.10706028
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. ©2024 IEEE.
Author Keywords	fast object tracking; Kalman filtering; optical flow; smart city; vehicle identification; vehicle tracking

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