Following the leader using a tracking system based on pre-trained deep neural networks

In this work, we present a software architecture to solve, at some level, the follow the leader problem. This problem consists of an autonomous vehicle trying to track and follow a leader vehicle. To track the leader position in consecutive camera images, we employed the Generic Object Tracking Usin...

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Bibliographic Details
Published in2017 International Joint Conference on Neural Networks (IJCNN) pp. 4332 - 4339
Main Authors Mutz, Filipe, Cardoso, Vinicius, Teixeira, Thomas, Jesus, Luan F. R., Golcalves, Michael A., Guidolini, Ranik, Oliveira, Josias, Badue, Claudine, De Souza, Alberto F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
Autonomous vehicles
Cameras
deep neural networks
Feature extraction
Laser radar
Neural networks
Sensors
Software architecture
tracking
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Summary:In this work, we present a software architecture to solve, at some level, the follow the leader problem. This problem consists of an autonomous vehicle trying to track and follow a leader vehicle. To track the leader position in consecutive camera images, we employed the Generic Object Tracking Using Regression Networks (GOTURN). GOTURN is a pre-trained Deep Neural Network capable of tracking generic objects, without application-specific training or fine-tuning. The proposed software architecture was evaluated using a real autonomous vehicle, in four stretches of a University ring road. In all experiments, the autonomous vehicle was able to follow the leader's path with maximum root mean square error of 0.28m.
ISSN:2161-4407
DOI:10.1109/IJCNN.2017.7966404