Bacharelado em Ciência da Computação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/6
Siglas das Coleções:
APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso
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2 resultados
Resultados da Pesquisa
Item Semantic segmentation for people detection on beach images(2021-03-01) Monte, Leonardo de Araujo; Macário Filho, Valmir; http://lattes.cnpq.br/4346898674852080; http://lattes.cnpq.br/0547792731866043Cameras monitoring are increasingly aided by computer vision systems that identify risk situations. This work is part of an automatic track system to monitor beaches in the metropolitan area of Recife in order to prevent bathers to trespass the boundaries of the safe region for swimming. Semantic segmentation has gained strength in several computer vision tasks. Usually, the metaarchitecture of a semantic segmentation network consists of two modules: encoder (backbone) and decoder. This work does a study combining a set of semantic segmentation networks, Unet, Xnet, LinkNet and Unet++ with the pretrained backbones VGG16 and VGG19, to detect swimmners in beach images. We have used our own dataset, made by several images taken at the Boa Viagem beach, RecifeBrazil. The algorithms are evaluated with MIoU metric regarding the entire image scene and just in the water area. The best MIoU regarding all image was 80.87best MIoU in detecting swimmers at the beach was 85.56obtained by the LinkNet algorithm with both VGG16 and VGG19 backbones.Item Estudo de viabilidade de sistemas de detecção de armamentos em tempo real em linhas de ônibus urbanos(2021-12-09) Lima Junior, Cícero Pereira de; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/9901763283774954Surveillance systems are fundamental on preventing armed robberys on public busses. However, to be operated in real-time theses systems demand an unrealistic amount of people. The usage of computer vision and deep learning technics raises as a way to automate parts or even the whole surveillance process, from the weapons detection to the alarm triggering. For this process to be accomplished efficiently, allowing authorities to take more effective actions, the system needs to be able to handle a growing security cameras demand. Thus, this work analyses a bus line weapon detection system viabillity. Through simulation, this work evaluated the perfomance of YOLO algorithm, in its fourth version, on a client-server model under a growing security camera demand. The server is composed of a Tesla V80 GPU with a 12GB memory, Intel Xeon dual core processor, 61GB RAM memory and 200GB disk space. Finally, from the gathered results, its observable that the application presents a detection time increase after having introduced 16 virtual users (cameras), also the average response time cannot be considered as real-time, on bus security context.