Toward a pedestrian detection method by various feature combinations

Abstract

Pedestrian detection has been a crucial issue over the last decades. The existing pedestrian detection methods are still face abrupt illumination, partial occlusion, different poses of humans, and cluttered backgrounds challenges. Consequently, the significance of pedestrian detection systems encourages us to propose a new method to address some of these challenges and offer higher accuracy rate. Noting that the power of various kinds of features are different and a single type of feature cannot extract the comprehensive information of human shape. Taking this fact into consideration, we combined pragmatic and useful features in order to detect pedestrian more accurate. Indeed, we combine histogram of oriented gradients (HOG), a proposed modified local binary pattern (M-LBP), and a proposed modified Haar-like features (M-Haar) to achieve these goals. By applying the proposed method, it is possible to extract various information on human shapes including the edge information, texture information, and local shape information. After feature extraction, Cascade Adaboost classifier is used to detect pedestrian images from non-pedestrian. In experiments, INRIA dataset, Daimler dataset, and ETH dataset are applied. The extensive experimental results demonstrate that our approach outperforms the traditional methods in terms of the accuracy and robustness. © 2019 - IOS Press and the authors. All rights reserved.