title: Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering.
abstract: Lane boundary detection technology has progressed rapidly over the past few decades. However, many challenges that often lead to lane detection unavailability remain to be solved. In this paper, we propose a spatial-temporal knowledge filtering model to detect lane boundaries in videos. To address the challenges of structure variation, large noise and complex illumination, this model incorporates prior spatial-temporal knowledge with lane appearance features to jointly identify lane boundaries. The model first extracts line segments in video frames. Two novel filters-the Crossing Point Filter (CPF) and the Structure Triangle Filter (STF)-are proposed to filter out the noisy line segments. The two filters introduce spatial structure constraints and temporal location constraints into lane detection, which represent the spatial-temporal knowledge about lanes. A straight line or curve model determined by a state machine is used to fit the line segments to finally output the lane boundaries. We collected a challenging realistic traffic scene dataset. The experimental results on this dataset and other standard dataset demonstrate the strength of our method. The proposed method has been successfully applied to our autonomous experimental vehicle.
authors.affiliation: Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China. nanzhixiong@stu.xjtu.edu.cn.
authors.forename: Zhixiong
authors.initials: Z
authors.lastname: Nan
authors.affiliation: Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China. pingwei@xjtu.edu.cn.
authors.forename: Ping
authors.initials: P
authors.lastname: Wei
authors.affiliation: Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China. xlh@xjtu.edu.cn.
authors.forename: Linhai
authors.initials: L
authors.lastname: Xu
authors.affiliation: Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China. nnzheng@xjtu.edu.cn.
authors.forename: Nanning
authors.initials: N
authors.lastname: Zheng
keywords: crossing point filter; lane detection; spatial-temporal knowledge; structure triangle filter