A Hierarchical Scheme for Vehicle Make and Model Recognition From Frontal Images of Vehicles

Abstract

This paper presents a novel recognition scheme for vehicle make and model recognition (VMMR) from frontal images of vehicles. In general, we introduce some domain knowledge to cope with this task. The structural components contained in the frontal appearance of vehicles present different visual characteristics and their discriminating ability varies when vehicle models belonging to the same brand or different brands are compared. In light of the particularities, we take advantage of the varying discriminating ability of these structural components to perform the recognition task sequentially in two stages. At the first stage, the logo sub-region (which is one of the component-related sub-regions in the region of interest) is applied to classify the vehicle models at the brand level. Different from the traditional brand-level classification that the models of the same brand are considered as a single class, in this paper, multiple sub-classes in one brand class are allowed, since the intra-brand models also exhibit a certain degree of diversity. In this way, the problem of inter-class similarity is remitted. At the second stage, several customized classifiers are trained for each sub-class in the light of the discriminant ability of the remaining sub-regions. The proposed approach has been tested on a large-scale vehicle image database collected in this paper and has achieved the state-of-the-art results.