Taming the HoG: The Influence of Classifier Choice on Histogram of Oriented Gradients Person Detector Performance
[ 1 ] Instytut Automatyki, Robotyki i Inżynierii Informatycznej, Wydział Elektryczny, Politechnika Poznańska | [ S ] student | [ P ] employee
2017
chapter in monograph / paper
english
- object detection
- machine learning
- histogram of oriented gradients
EN Histogram of oriented gradients (HoG) is a common choice for hand-crafted feature used in a wide range of machine vision task. It functions as a part of a processing pipeline, in which it’s followed by a classifier. The canonical approach proposed by the authors of HoG is the use of a linear support vector machine (SVM). This approach is usually followed by the majority of adopters with good results. However, a range of classifiers have proven to perform better than linear SVM in a variety of applications. In this paper, we investigate the pairing between HoG and a range of classifiers in order to find one with the best performance in terms of accuracy and processing speed for the task of human silhouete detection.
27.05.2017
552 - 560
20
20
WoS (15)