Robust feature point detectors for car make recognition

Abstract

An Automatic Vehicle Make and Model Recognition (AVMMR) system can be a useful add-on tool to Automatic Number Plate Recognition (ANPR) to address potential car cloning, including intelligence collection by the police to outline past and recent car movement and travel patterns. Although several AVMMR systems have been proposed, the approaches perform sub-optimally under various environmental conditions, including occlusion and/or poor lighting distortions. This paper studies the effectiveness of deploying robust local feature points that can address these limitations. The proposed methods utilize a modification of two-dimensional feature points such as SIFT, SURF, etc. and their combinations. When SIFT gets combined with the multi-scale Harris/multi-scale Hessian methods, it could outperform existing approaches. Experimental evaluations using 4 different benchmark datasets are conducted to demonstrate the robustness of the proposed techniques and their abilities to detect and identify car makes and models under various environmental conditions. SIFT- DoG, SIFT- multiscale Hessian, and SIFT- multiscale Harris are shown to yield the best results for our datasets with higher recognition rates than those achieved with other existing methods in the literature. Therefore, it can then be concluded that the combination of certain covariant feature detectors and descriptors can outperform other methods.