Overtaking Vehicle Detection Techniques based on Optical Flow and Convolutional Neural Network

Lu-Ting Wu, Huei-Yung Lin

Abstract

As the rise of the intelligent vehicle applications in recent years, the development of onboard vision systems for advanced driving assistance has become a popular research topic. This paper presents a real-time system using a monocular camera mounted on the rear of a vehicle to perform overtaking detection for safe lane change operations. In this work, the possible overtaking vehicle is first located based on motion cues. The candidate is then identified using Convolutional Neural Network (CNN) and tracked for behavior analysis in a short period of time. We also propose an algorithm to solve the issue of repetitive patterns which is commonly appeared in the highway driving. A series of experiments are carried out with real scene video sequences recorded by a dashcam. The performance evaluation has demonstrated the effectiveness of the proposed technique.

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Paper Citation


in Harvard Style

Wu L. and Lin H. (2018). Overtaking Vehicle Detection Techniques based on Optical Flow and Convolutional Neural Network.In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-293-6, pages 133-140. DOI: 10.5220/0006698001330140


in Bibtex Style

@conference{vehits18,
author={Lu-Ting Wu and Huei-Yung Lin},
title={Overtaking Vehicle Detection Techniques based on Optical Flow and Convolutional Neural Network},
booktitle={Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2018},
pages={133-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006698001330140},
isbn={978-989-758-293-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Overtaking Vehicle Detection Techniques based on Optical Flow and Convolutional Neural Network
SN - 978-989-758-293-6
AU - Wu L.
AU - Lin H.
PY - 2018
SP - 133
EP - 140
DO - 10.5220/0006698001330140