Zebra Crossing Detection Using Concatenation of SIFT-Gabor Features

Abstract

Zebra-crossing recognition is a challenging activity for visually impaired navigating safely. This paper proposes a zebra-crossing scene recognition model-based fusion of texture features and geometrical features. The Gabor features for 8 orientations are extracted for different orientations Zebra Crossing lines. The SIFT is employed for keypoint detection and description. The final features vector is obtained by concatenating Gabor and SIFT features. The feature vector is optimized using K-means clustering and Principle Component Analysis (PCA). The optimized feature vectors were classified using five Classifiers, SVM, Random Forest, Decision Tree, Naïve Bayes Classifiers. The proposed model successfully recognizes the crosswalks with 89% accuracy. The different classifier models are evaluated for Precision, Accuracy, F1 Score, Recall, and RoC. This paper specifies the classification of two class positive which is zebra-crossing and negative which is no zebra-crossing. The designed system will classify either the crosswalk is detected or not on the basis of percentage specified for each class. This can be done by matching the features of the trained dataset. Those features are matched with the real videos or images to recognize the motion and classify into specific class. More specific crosswalk detection in real time can be executed to get more specific type of positive and negative class and classification.