Recognition and Tracking of Vehicles in Highways using Deep Learning

• Ano de defesa: 2019
• Autor(a) principal: Cala, Ludwin Lope
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Aprendizado profundo; Computer vision; Deep learning; Detecção e classificação; Detection and classification; Drone; Rastreamento; Recurrent neural network; Rede neural recorrente; Tracking; Visão computacional
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-02082019-164853/

Unmanned aerial vehicles (UAV) have become increasingly popular and their ability to analyze images collected in real time has drawn the attention of researchers regarding their use in several tasks, as surveillance of environments, persecution, collection of images, among others. This dissertation proposes a vehicle tracking system through which UAVs can recognize a vehicle and monitor it in highways. The system is based on a combination of bio-inspired machine learning algorithms VOCUS2, CNN and LSTM and was tested with real images collected by an aerial robot. The results show it is simpler and outperformed other complex algorithms, in terms of precision.