Multi-stage fusion of local and global features based classification for traffic sign recognition

Abstract

The automatic traffic sign detection and recognition (TSDR) provide an additional level of driver assistance, leading to increase passengers, road users and vehicles safety. As part of Advanced Driving Assistance Systems (ADAS), traffic sign recognition (TSR) has drawn considerable research attention in recent years due to its challenging nature as a computer vision problem. It is usually tackled in three stages: detection, feature extraction and classification. This paper focuses on the second stage of the process, namely traffic sign feature extraction and proposes to fuse two discriminative and complementary feature sets. In this approach, Discrete Cosine Transform (DCT) is used to extract global features of traffic sign while Local Binary Patterns (LBP) is used to extract local descriptors. The classification of these features is performed using the Support Vector Machine (SVM). The proposed fusion approach is validated on the German Traffic Sign Recognition Benchmark Dataset (GTSRD) and has been found to be more efficient than a recognition system which uses only one feature, trained individually.