Application of neural networks for vehicle classifiers: Extreme learning machine approach

Abstract

Machine learning has been a popular topic in research field for many applications. One of the applications is traffic surveillance system. In many areas, traffic surveillance system is installed in order to gather and estimate important traffic information. Nowadays, there are several systems used for information's extracting, and classifying. One of the well-known approaches is decision tree. It uses a tree-like model that decides consequences outcomes from events. However, in some application, decision tree does not perform well. Another widely used approach is neural network, which has promising performance. It has been developed and become one of the most popular computing systems in research field. The traditional approach in training neural network is backpropagation. However, it has several drawbacks. One of them is the training time. In recent decades, Extreme learning machine (ELM) was proposed for training single hidden layer feed-forward neural network (SLFN) in the extremely fast way. It minimizes training error by utilizing dataset in one-shot calculation. This paper focuses on classifiers in traffic surveillance system. The classification divides into two main tasks. One is vehicle types' classification. Another is vehicle colors' classification. Neural networks trained with ELM are applied to the dataset. The performance are then compared to decision tree based approaches with ensemble methods. The experimental results show that ELM achieves better accuracy than of decision tree based approaches in both tasks.