Estudo comparativo de detecção e rastreamento de elementos no trânsito utilizando imagens ominidirecionais
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Data
2024-11-22
Autores
Scarparo, Heisthen Mazzei
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Universidade Federal do Espírito Santo
Resumo
Traffic detection and tracking play an important role in the context of smart cities. These technologies have the potential to alleviate congestion, optimize the use of resources, and improve the quality of life of the population. However, one aspect of this field that has not yet been explored is the use of omnidirectional videos, which provide a 360° field of view. Omnidirectional images offer a large field of view of the road environment, allowing for a more complete analysis of traffic and moving objects. This panoramic view makes it possible to detect vehicles, pedestrians, cyclists, and other elements in all directions, including angles that are difficult to capture with conventional cameras. Using this type of imagery for traffic light control makes it easier to obtain information on the trajectory of vehicles in real time and, therefore, configure traffic lights in a more intelligent and efficient way. In addition, omnidirectional images can be used to monitor areas of high traffic density, identify congestion points, and analyze road user behavior patterns. This information is valuable for urban planning, the development of mobility policies, and the implementation of strategies aimed at improving traffic flow and street safety. Although the use of 360° panoramic images in the context of traffic detection and tracking is still an underexplored f ield, it represents a good tool for the implementation of smart cities through its integration with traffic light control and traffic management systems in cities. In this context, this work presents a database containing 25 panoramic videos, with their respective annotations. This database is available for use by the academic community. It also presents a comparative study between the application of the YOLOv5, YOLOv7, and YOLO-NAS networks, together with the use of the DEEPSORT algorithm, for detection and tracking of traffic objects present in the database. To compare the networks, the metrics of Precision, Recall, F1-Score, mAP@.5, and mAP@.5:.95 were used. In this study, the best result was obtained using the YOLOv7 network with training. Such result shows the feasibility of considering the use of omnidirectional images as a tool in the task of traffic monitoring and helping provide urban mobility
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Palavras-chave
Detecção de objetos de trânsito , Rastreamento de objetos de trânsito , Dataset de objetos de trânsito com imagens Ominidirecionais , Cidades Inteligentes , Estudo comparativo , Imagens Ominidirecionais , Rastreamento , Detecção , YOLO , Deepsort , Dataset , Comparative study , Omnidirectional images , Tracking , Detection