Vehicle Recognition Based on Local Feature

Abstract

This paper introduced a vehicle recognition system based on Speed-up Robust Feature (SURF) and bag-of-features (BOF). In the system, we extract SURF features of vehicle images, and use k-means algorithm to analyze the features, clustering center of the features, so we can get some “visual words”. Then we describe all images use those “words”, and generate histogram to quantize the features. In this paper, term frequency- inverse document frequency (tf-idf) is used to weight the features to weaken the influence of useless features. We have 22 different type and brand of vehicle, including sedan, SUV, bus and so on. The experiment shows that this way can make the recognition accuracy over 90%.