An Ensemble Broad Learning Scheme for Semisupervised Vehicle Type Classification
Li Guo, Runze Li, Bin Jiang
Abstract
Nowadays vehicle type classification is a fundamental part of intelligent transportation systems (ITSs) and is widely used in various applications like traffic flow monitoring, security enforcement, and autonomous driving, etc. However, vehicle classification is usually used in supervised learning, which greatly limits the applicability for real ITS. This article proposes a semisupervised vehicle type classification scheme via ensemble broad learning for ITS. This presented method contains two main parts. In the first part, a collection of base broad learning system (BLS) classifiers is trained by semisupervised learning to avoid time-consuming training process and alleviate the increasingly unlabeled samples burden. In the second part, a dynamic ensemble structure constructed by trained classifier groups with different characteristics obtains the highest type probability and determine which the vehicle belongs, so as to achieve superior generalization performance than a single base classifier. Several experiments conducted on the pubic BIT-Vehicle dataset and MIO-TCD dataset demonstrate that the proposed method outperforms single BLS classifier and some mainstream methods on effectiveness and efficiency.