An Effective and Robust Multi-view Vehicle Classification Method Based on Local and Structural Features

Abstract

Big traffic data analysis for intelligent transportation is attracting more and more attention. Due to different designs of vehicles in the same class and the similarity of shape and textures between different classes, vehicle classification is remaining a challenge. In this paper, different from traditional methods that only classify vehicles to two or three types in one viewpoint, a novel method using local and structural features has been proposed for vehicle classification in real-time traffic system that has a good ability to categorize vehicles into more specific types and is robust to the changes in viewpoint. Specifically, local features are obtained using scale invariant feature transform (SIFT), and an efficient L2-norm sparse coding technique is used to reduce computational cost. Besides, vehicle building structures are extracted as structural features. Finally, linear support vector machine (SVM) is used as the classifier. The performance evaluations using real vehicle images extracted from surveillance videos in different viewpoints are carried out and five vehicle classes (SUV, truck, van, bus, car) are considered. Experimental results show that the proposed method can obtain an average accuracy of 95.95% in real-time, which validate the effectiveness of our method.