Pedestrians detection using a cascade of LBP and HOG classifiers

Abstract

Accurate pedestrian detection in urban environment is a highly explored research field. We propose a new approach in pedestrian detection that combines the popular Local Binary Patterns and Histogram of Oriented Gradient features. The novelty of our work resides in the combination of a reduced HOG feature vector with uniform LBP patterns for the pedestrian data representation. Another contribution resides in the design and implementation of a two-stage cascade classifier of Support Vector Machine. Our method has been trained and tested on reference benchmark datasets and it proved to have good results.