A Coarse-to-Fine Strategy for Vehicle Logo Recognition from Frontal-View Car Images

This paper proposes a vehicle logo recognition (VLR) system centered on front-view cars, which has been largely neglected by vision community in comparison to other object recognition tasks. The study focuses on local features that describe structural characteristics by locating the logo of a car using a coarse-to-fine (CTF) strategy that first detects the bounding box of a car then the grille and at last, the logo. The detected logo is then used to recognize the make of a car in a reduced time. Our system starts to progress in detecting the bounding box of a car by means of a vocabulary voting and scale-adaptive mean-shift searching strategy. The system continues to process in locating the bounding box of an air-intake grille using a scale-adaptive sliding window searching technique. In the next level, the bounding box of a logo is located by means of cascaded classifiers and circular region detection techniques. The classification of vehicle logos is carried out on the patch-level as occurrences of similar visual words from a visual vocabulary, instead of representing the patchbased descriptors as bag-of-features and classifying them using a standard classifier. The proposed system was tested on 25 distinctive elliptical shapes of vehicle logos with 10 images per class. The system offers the advantage of accurate logo recognition of 86.3% in the presence of significant background clutter. The proposed scheme could be independently used for part recognition of grille detection and logo detection.