Method for Preceding Vehicle Type Classification Based on Sparse Representation

Abstract

This paper proposes a novel vehicle-type classifier named SRCVT that uses video data collected from video detection units. The SRCVT uses the sparse representation classifier (SRC) technique without the requirement of an additional training procedure to construct the classification model. It classifies preceding vehicles directly from the testing samples’ sparse representation, without the need for explicit model selection. The SRCVT consists of four steps: data preparation, principal component analysis transformation, realization, and classification output. The classifier has been compared with the traditional method of using a supported vector machine. The results show that the SRCVT is more promising for vehicle-type classification in terms of classification accuracy and ease of use.