RT Conference Proceedings
T1 Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study
A1 Espinosa, Jorge E.
A1 Velastin Carroza, Sergio Alejandro
A1 Branch, John W.
AB This paper presents a comparative study of two deep learning models used here for vehicle detection. Alex Net and Faster R-CNN are compared with the analysis of an urban video sequence. Several tests were carried to evaluate the quality of detections, failure rates and times employed to complete the detection task. The results allow to obtain important conclusions regarding the architectures and strategies used for implementing such network for the task of video detection, encouraging future research in this topic.
PB Springer
SN 978-3-319-70009-0
YR 2017
FD 2017-11-29
LK https://hdl.handle.net/10016/28912
UL https://hdl.handle.net/10016/28912
LA eng
NO This paper has been presented at : 5th International Visual Informatics Conference (IVIC 2017)
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 wish to thank Dr. Fei Yin for the code for metrics employed for evaluations. Finally, we 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 30 jun. 2024