ITM Web Conf.
Volume 59, 2024II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
|Number of page(s)
|Adaptive Intelligence: Exploring Learning in Evolutionary Algorithms and Neural Networks
|25 January 2024
Optimized zone-based vehicle speed estimation and classification
Moscow Institute of Physics and Technology (MIPT),
* Corresponding author: email@example.com
Abstract. This article pioneers the fusion of advanced computer vision, and environmental science in order to be a starting point in ecological tasks and environmental benefits. Utilizing state-of-the-art tools like YOLOv7 and innovative algorithms, the study achieves unmatched accuracy in vehicle identification, classification, tracking, and speed analysis. By optimizing YOLOv7-e6e-1280 architecture using TensorRT and reduced precision, real-time analysis becomes possible without compromising accuracy. The integration of the Vanishing Point Principle for road zoning and zone-based speed calculation provides nuanced insights into driving behaviors. Detailed vehicle classification and robust tracking offer valuable data for urban planning and ecological studies. This approach increase our potential in vehicular analysis, setting new standards for research in urban development, transportation, and environmental science.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.