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Design and Development of a Robotic Arm for Rehabilitation and Training Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Cascade-Adaboost for Pedestrian Detection Using HOG and Combined Features

Authors : Gyujin Jang, Jinhee Park, Moonhyun Kim

Published in: Advances in Computer Science and Ubiquitous Computing

Publisher: Springer Singapore

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Abstract

Over the recent years, pedestrian detection beings in a video surveillance system is attracting more attention due to its wide range of applications. In this paper, we propose an efficient two-phase pedestrian detector using HOG and combined features. The detector finds pedestrian candidate regions with a cascade-adaboost on HOG features. It then verifies each candidate using a combined features, which is local (SURF) and global features (RGB histogram), and then a classification based on MLP. It obtains a better detection rate and false-positive rate. The pedestrian detection system experimented with PETS 2009 dataset proves the effectiveness of our detection model.

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Metadata
Title
Cascade-Adaboost for Pedestrian Detection Using HOG and Combined Features
Authors
Gyujin Jang
Jinhee Park
Moonhyun Kim
Copyright Year
2017
Publisher
Springer Singapore
Book
Advances in Computer Science and Ubiquitous Computing

Print ISBN: 978-981-10-3022-2

Electronic ISBN: 978-981-10-3023-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-3023-9_67