Fast pedestrian detection using BWLSD for ROI (opens in a new tab)

Abstract

This paper presents a pedestrian detection approach based on Multi-block Local Binary Pattern (MB-LBP) features and Weighted Region Covariance Matrix (WRCM). Multistage classifiers are used to increase the processing speed and reliability of the detection system. Using the modified 3-D B-spline Wavelet-Based Local Standard Deviation (BWLSD) techniques, the region of interest is determined. Once the pedestrian region is identified, the front end of the multistage classifier quickly determines wherever pedestrians may be present, while the back end confirms whether the first descriptor did classify correctly. The experimental results demonstrated that our approach performed well in real-time application.