MKL-SING: A data-driven approach of sign recognition for managing and improving public services - Zhou, Jin; Tian, Xinlu (Information processing & management, 2023)
In this paper, we mainly have the followings lights:•The improved CLAHE algorithm is used to enhance the image. Firstly, the original image is transformed from RGB space to HIS space. In HIS color space, the contrast enhancement of i space and the saturation enhancement of s space are carried out to achieve the dual enhancement of contrast and color saturation of low-contrast images.•A multi-kernel support vector machine (SVM) algorithm based on feature weighting is designed to complete the classification of video traffic signals, and the properties of multi-kernel SVM are proved. Develop multi-core SVM based on feature weighting to complete the classification of traffic signals and improve the performance of traditional single-core SVM. SVM can project each kernel function into high-dimensional feature space to obtain global solutions, thus solving nonlinear optimization problems. The detection and identification of traffic signs is a fundamental function of an intelligent transportation system. The extraction or identification of a road sign poses the same problems as object identification in natural contexts: conditions of illumination are variable and uncontrollable, and various objects frequently surround road signs. These difficulties make the extraction of features difficult. The fusion of time and space features of traffic signs is important for improving the performance of sign recognition. Deep learning-based algorithms are time-consuming to train based on a large amount of data. They are difficult to deploy on resource-constrained portable devices and conduct sign detection in real time. The accuracy of sign detection should be further improved, which is related to the safety of traffic participants. To improve the accuracy of feature extraction and classification of traffic signs, we propose MKL-SING, a hybrid approach based on multi-kernel support vector machine (MKL-SVM) for public transportation SIGN recognition. It contains three main components: a principal component analysis for image dimension reduction, a fused feature extractor, and a multi-kernel SVM-based classifier. The fused feature extractor extracts and fuses the time and space features of traffic signs. The multi-kernel SVM then classifies the traffic signs based on the fused features. Different kernel functions in the multi-kernel SVM are fused based on a feature weighting procedure. Compared with single-core SVM, multi-kernel SVM can better process massive data because it can project each kernel function into high-dimensional feature space to get global solutions. Finally, the performance of SVM-TSR is validated based on three traffic sign datasets. Experiment results show that SVM-TSR performs better than state-of-the-art methods in terms of dynamic traffic sign identification and recognition.