Abstract: Traffic sign recognition (TSR) is an important research direction of intelligent transportation system (ITS). Feature extraction is the key point of traffic sign recognition research. This paper focuses on feature extraction of traffic sign recognition and summarizes common manual features and depth features. Manual features include color histogram, scale invariant feature transformation feature, local binary pattern feature, directional gradient histogram feature, Haar-like feature, Gabor wavelet feature, Canny feature, etc. Depth features are extracted from AlexNet, VGG16, Inception, etc. Various features are extracted from the same data set (GTSRB). Various features are compared and analyzed quantitatively by using the same classifier and the same evaluation index system. This paper makes an intuitive comparative research of performance for different features and different types of traffic signs by means of charts and graphs, aiming at providing a reference for the selection of feature vectors and for the further research of traffic sign recognition.

Key words: TSR, ITS, feature extraction, manual features, depth features