Vehicle Type Classification Using PCA with Self-Clustering

Abstract

Different conditions, such as occlusions, changes of lighting, shadows and rotations, make vehicle type classification still a challenging task, especially for real-time applications. Most existing methods rely on presumptions on certain conditions, such as lighting conditions and special camera settings. However, these presumptions usually do not work for applications in real world. In this paper, we propose a robust vehicle type classification method based on adaptive multi-class Principal Components Analysis (PCA). We treat car images captured at daytime and night-time separately. Vehicle front is extracted by examining vehicle front width and the location of license plate. Then, after generating eigenvectors to represent extracted vehicle fronts, we propose a PCA method with self-clustering to classify vehicle type. The comparison experiments with the state of art methods and real-time evaluations demonstrate the promising performance of our proposed method. Moreover, as we do not find any public database including sufficient desired images, we built up online our own database including 4924 high-resolution images of vehicle front view for further research on this topic.