Locality constraint distance metric learning for traffic congestion detection

In this paper, a locality constraint distance metric learning is proposed for traffic congestion detection. First of all, an accurate and unified definition of congestion is proposed and the congestion level analysis is treated as a regression problem in the paper. Based on that definition, a dataset consists of 20 different scenes is constructed for the first time since the existing dataset is not diverse for real applications. To characterize the congestion level in different scenes, the low-level texture feature and Kernel Regression is utilized to detect traffic congestion level. To reduce the influence among different scenes, a Locality Constraint Distance Metric Learning (LCML) which ensured the local smoothness and preserved the correlations between samples is proposed. The extensive experiments confirm the effectiveness of the proposed method.