Robust lane detection through automatic trajectory analysis with deep learning and big data environment
Author(s)
Author(s)
Wang, L.-W.
Li, D.
Lun, D. P.-K.
Date Issued
2022
Publisher
Society of Photo-Optical Instrumentation Engineers (SPIE)
Related Publication(s)
Proceedings of the International Workshop on Advanced Imaging Technology (IWAIT 2022)
Abstract
This paper gives focus on multi-lane detection from traffic cameras, which is based on automatic trajectory analysis and is promoted with advanced deep-learning technologies. Our proposed approach is based on big trajectory data that is robust to complex road scenes, which makes our approach particularly reliable and practical for Intelligent Transportation Systems. By using the deep learning object detection technology, it firstly generates big trajectory data on the road. Then, it detects the stop lines on the road and counts the number of lanes from the trajectories. Next, the trajectories are divided into different groups, where each group contains the trajectories of one lane. Finally, the lanes are fitted by the grouped trajectories. A large number of experiments have been done. The results show that the proposed approach can effectively detect the lanes on the road.
SFU Affiliated Publication
Yes
Availability at SFU Library
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