TCC - Bacharelado em Ciência da Computação (Sede)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/415
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Item Rastreamento de pedestres 3D multi-câmera usando redes neurais de grafos(2022-05-27) Andrade, Isabella Stefanny Fernandes de; Lima, João Paulo Silva do Monte; http://lattes.cnpq.br/1916245590298485; http://lattes.cnpq.br/5529506615862118Tracking the position of pedestrians over time through camera images is a rising computer vision research topic. In multi-camera settings, the researches are even more recent. Many solutions use supervised neural networks to solve this problem, which can require a lot of effort to annotate the data in addition to a lot of time spent to train the network. The goals of this work are: develop variations of pedestrian tracking algorithms, being desirable to avoid the need to have annotated data; and compare the results obtained through accuracy metrics. Therefore, this work proposes an approach for tracking pedestrians in 3D space in multi-camera environments using the Message Passing Neural Network framework inspired by graphs. We evaluated the solution using the WILDTRACK dataset and a generalizable detection method, reaching 77.1% of MOTA when training with data obtained by a generalizable tracking algorithm. The algorithm can track at a 40 frames per second rate.