摘要
In this paper, approaches are presented for the thresholding, detection, tracking and recognition of the road signs as part of an Advanced Driver Assistance System (ADAS). In all these approaches, feature extraction is the backbone, whereas detection and recognition require the use of detectors and classifiers, respectively. In this, two issues are dominant: 1) Tackling the variability involved in the lighting conditions, sizes, and shapes of the road signs after segregating them from a world scene, and 2) Focusing on inaccurate fuzzy modeling arising due to the improper distribution of pixel intensities. The variability is overcome with the uncertainty representation using the information sets, an extension of fuzzy sets, whereas the incorrect fuzzy modeling is rectified using the pervasive information sets, an extension of intuitionistic fuzzy sets. The development of the intuitionistic fuzzy transform paralleling the fuzzy entropy function paves the way for the formulation of different hesitancy features by cashing in on the non-membership function. Next, promulgation of the Hanman law prescribes the fuzzy gradient/divergent values for different tasks. The notable landmarks of this work are the creation of a Color-Based Detector (CBD), derivation of the incremental hesitancy features accrued from the color histograms and the formulation of a variant of the Hanman Transform Classifier using Convolutional Neural Network (CNN) features. We have used the Belgium dataset to vindicate the efficacy of the proposed methods.
In this paper, approaches are presented for the thresholding, detection, tracking and recognition of the road signs as part of an Advanced Driver Assistance System (ADAS). In all these approaches, feature extraction is the backbone, whereas detection and recognition require the use of detectors and classifiers, respectively. In this, two issues are dominant: 1) Tackling the variability involved in the lighting conditions, sizes, and shapes of the road signs after segregating them from a world scene, and 2) Focusing on inaccurate fuzzy modeling arising due to the improper distribution of pixel intensities. The variability is overcome with the uncertainty representation using the information sets, an extension of fuzzy sets, whereas the incorrect fuzzy modeling is rectified using the pervasive information sets, an extension of intuitionistic fuzzy sets. The development of the intuitionistic fuzzy transform paralleling the fuzzy entropy function paves the way for the formulation of different hesitancy features by cashing in on the non-membership function. Next, promulgation of the Hanman law prescribes the fuzzy gradient/divergent values for different tasks. The notable landmarks of this work are the creation of a Color-Based Detector (CBD), derivation of the incremental hesitancy features accrued from the color histograms and the formulation of a variant of the Hanman Transform Classifier using Convolutional Neural Network (CNN) features. We have used the Belgium dataset to vindicate the efficacy of the proposed methods.
