Efficient Human Detection Based on Parallel Implementation of Gradient and Texture Feature Extraction Methods

Pedestrian Detection is of interest in many computer vision applications such as intelligent transportation systems and human-robot interaction; among the existing methods, the combination of shape feature (i.e. Histogram of Oriented Gradients (HOG)) and texture features (i.e. Local Binary Pattern (LBP)) has shown promising results in detection accuracy, but it is limited due to computation cost. In this paper, we introduce a new pedestrian detection algorithm with fast computation of these features on GPU. We propose a robust and rapid pedestrian detector by combining the HOG with LBP, as the feature set and corresponding Support Vector Machine (SVM) classifiers. Also, we use the integral image method and an efficient parallel implementation to reduce detection time. We can achieve a more than 10× speed up, and 7% increase in detection rate.