A Multi-Feature Fusion Based Vehicle Logo Recognition Approach for Traffic Checkpoint. Issue 2 (February 2020)

• Record Type: Journal Article
• Authors: Ding, Sha; Wu, Hongyang
• Is Part Of: IOP conference series. Volume 440:Issue 2(2020)
• Publication Date: 2020-02
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/440/2/022071
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: In this paper, we propose a vehicle logo recognition algorithm based on multifeature fusion using a hierarchical classification approach, which can be applied at traffic checkpoints. First, a typical database of vehicle logos is set up based on surveillance images recorded at traffic checkpoints. Next, three features, HOG, Curvature histograms, and GIST, are extracted and three corresponding first level classifiers are trained using the support vector machine (SVM) algorithm. The probability that a certain test sample belongs to a certain kind can be obtained by predicting the sample with each level-1 classifier. All these probabilities are then concatenated and used as features for training a second-level SVM classifier. The resultant new classifier is used for classifying the vehicle logos of the test set. The experimental results show that the proposed approach to hierarchically integrate multiple features provides excellent accuracy for the vehicle logo recognition task.