Pedestrian Recognition Based on Multi-Scale Weighted HOG

Abstract

Pedestrian recognition receives a great attention in recent years due to its importance in traffic accidents identification. Traffic surveillance systems can provide valuable information for pedestrian recognition using computer vision and image processing techniques. Most techniques exploit simple feature extraction and multi-stage feature matching to train classifiers. In this study, Canny edge information and Histogram of Oriented Gradient (HOG) has been integrated into multi-scale coarse-to-fine feature extraction. Edge distribution provides a variable weight to highlight distinctive gradients in a Multi-Scale Weighted HOG (MS-WHOG) to identify pedestrian. As a result, the pedestrian distinctive features are highlighted and expanded along the edges to improve the recognition process. The proposed technique is also scale and orientation invariant, due to the use of multi-scale and edge information.