RT Conference Proceedings
T1 Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN
A1 Espinosa, Jorge E.
A1 Velastin Carroza, Sergio Alejandro
A1 Branch, John W.
AB This paper introduces a Deep Learning Convolutional Neutral Network model based on Faster-RCNN for motorcycle detection and classification on urban environments. The model is evaluated in occluded scenarios where more than 60% of the vehicles present a degree of occlusion. For training and evaluation, we introduce a new dataset of 7500 annotated images, captured under real traffic scenes, using a drone mounted camera. Several tests were carried out to design the network, achieving promising results of 75% in average precision (AP), even with the high number of occluded motorbikes, the low angle of capture and the moving camera. The model is also evaluated on low occlusions datasets, reaching results of up to 92% in AP.
PB The Institution of Engineering and Technology
SN 978-1-78561-887-1
YR 2018
FD 2018-05-22
LK https://hdl.handle.net/10016/28952
UL https://hdl.handle.net/10016/28952
LA eng
NO This paper has been presented at: 9th International Conference on Pattern Recognition Systems (ICPRS-18)
NO S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research. The data and code used for this work is available upon request from the authors.
DS e-Archivo
RD 18 jul. 2024