Recognition and Tracking of Vehicles in Highways using Deep Learning
Ludwin Lope Cala1; Roseli Aparecida Francelin Romero1
1 Instituto de Ciências Matemáticas e de Computação - USP
Baixar PDF doi:10.20906/CPS/CBA2018-0720
Resumo
Unmanned aerial vehicles (UAVs) are becoming increasingly popular. Researchers are trying to use them in various tasks, such as, surveillance of environments, persecution, collection of images, etc. In many cases, it may be interesting that they have the ability to analyze the images that are being collected in real time. In this work, we propose a vehicle tracking system to turn UAVs able to recognize a vehicle and monitor it in highways. It is based on a combination of bio-inspired algorithms: VOCUS2, CNN and LSTM. The proposed system was tested with real images collected by the aerial robot and the results show that in spite of the proposed system is simpler than others, it achieved a good classification performance and overcame other existing approaches in terms of precision.
Palavras-chave: Computer Vision; Deep Learning; Recurrent Neural Network; Tracking; Detection and Classification, Drone.