Lane Detection by Combining Trajectory Clustering and Curve Complexity Computing in Urban Environments

Detecting lane automatically from the IP camera is an important component of the intelligent vision-based traffic big data system. Many previous studies focus on the main lanes detection task based clustering algorithm. However, some left-turning or right-turning lanes are ignored in these methods due to their seldom happening in real traffic scene. This paper attempts to address this issue. We try to detect those lanes which seldom appear in the trajectory lines by combining main lane detection and curve complexity computing method. Firstly, vehicles are detected by using SSD method. Secondly, a vehicle tracking method was employed to find all the trajectory lines. We recognized the left-turning lane among the entire main lane trajectory by computing its trajectory curve complexity. The main lanes were detected by using fuzzy k-means clustering method and the similarity computing method. A modified Hausidorff distance algorithm is incorporated and some experiments are conducted on an intersection of the urban environment to test its validity and efficiency.