Carvalho, Bruno Motta deSilva, Luiz Fernando Virginio da2017-11-072017-11-072017-07-31SILVA, Luiz Fernando Virginio da. Extraindo dados de tráfego a partir de vídeos em tempo real. 2017. 79f. Dissertação (Mestrado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2017.https://repositorio.ufrn.br/jspui/handle/123456789/24211Some of the major problems in large cities are related to urban mobility. Problems such as traffic jams and vehicle accidents directly impact society in a negative way, and are usually attributed to lack of urban planning from governments, the lack of public policies or research projects aimed at solving this problems, even if partially. These researche projects depend on data that must be collected in loco on the main avenues and streets of the city, that are now performed manually through the observation of images captured by CCTV cameras (Closed Circuit TV), the main means of traffic surveillance in the city. Thus, there is a need for a solution that is able to automaticaly collect these data in order to reduce costs with personnel, optimize the work and also reduce errors that arise from this operation. In this way, we propose a method capable of collecting this data automatically, in real time, using these video images to support the researche projects and explore possible actions in traffic management. Our method consists of a continuous flow of activities that use the collected images. First, it uses motion segmentation to detect moving objects. Then, we apply, in each segmented object, an adaptation of the Viola-Jones method to refine the search in the detection of vehicles, classifying them. In this step, we deal with occlusion situations, a common phenomenon of objects overlapping that directly interfere on results. Finally, we apply the Senior method to track each vehicle in order to obtain relevant traffic data, initially direction, speed and intensity of flow. We submit some videos collected on a large avenue to test our method. As a result, we construct an efficient model with low computational cost capable of handling situations of occlusion in distincts lighting conditions, which is the main contribution of this work.Acesso AbertoVigilância de tráfegoProcessamento de imagensVisão computacionalAprendizado de máquinaDetecçãoRastreamentomasterThesisCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO