NDLI: Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos

Content Provider	IEEE Xplore Digital Library
Author	Xianbin Cao Changxia Wu Pingkun Yan Xuelong Li
Copyright Year	2011
Description	Author affiliation: Center for OPTical Imagery Analysis and Learning (OPTIMIMAL), State Key Laboratory of Transient, Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shanxi, P.R. China (Pingkun Yan; Xuelong Li) \|\| University of Science and Technology of China, Hefei, 230026, P.R. China (Xianbin Cao; Changxia Wu)
Abstract	Visual surveillance from low-altitude airborne platforms has been widely addressed in recent years. Moving vehicle detection is an important component of such a system, which is a very challenging task due to illumination variance and scene complexity. Therefore, a boosting Histogram Orientation Gradients (boosting HOG) feature is proposed in this paper. This feature is not sensitive to illumination change and shows better performance in characterizing object shape and appearance. Each of the boosting HOG feature is an output of an adaboost classifier, which is trained using all bins upon a cell in traditional HOG features. All boosting HOG features are combined to establish the final feature vector to train a linear SVM classifier for vehicle classification. Compared with classical approaches, the proposed method achieved better performance in higher detection rate, lower false positive rate and faster detection speed.
Starting Page	2421
Ending Page	2424
File Size	719823
Page Count	4
File Format	PDF
ISBN	9781457713040
ISSN	15224880
e-ISBN	9781457713033
e-ISBN	9781457713026
DOI	10.1109/ICIP.2011.6116132
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-11
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Vehicles Boosting Training Feature extraction Vehicle detection Videos urban environment boosting HOG feature linear SVM
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

2011 18th ieee international conference on image processing linear svm classification using boosting hog features for vehicle detection in low-altitude airborne videos.

An efficient selection of HOG feature for SVM classification of vehicle

Vehicle Detection and Motion Analysis in Low-Altitude Airborne Video Under Urban Environment

Pedestrian Detection Based on Motion Compensation and HOG/SVM Classifier

Novel features selection for gender classification

Airborne vehicle detection using SURF-descriptors and Support Vector Machines

A HOG Feature and SVM Based Method for Forward Vehicle Detection with Single Camera

Pedestrian Detection Based on HOG-LBP Feature

Multiple Kernel Learning for vehicle detection in wide area motion imagery

Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos

Similar Documents

2011 18th ieee international conference on image processing linear svm classification using boosting hog features for vehicle detection in low-altitude airborne videos.

An efficient selection of HOG feature for SVM classification of vehicle

Vehicle Detection and Motion Analysis in Low-Altitude Airborne Video Under Urban Environment

Pedestrian Detection Based on Motion Compensation and HOG/SVM Classifier

Novel features selection for gender classification

Airborne vehicle detection using SURF-descriptors and Support Vector Machines

A HOG Feature and SVM Based Method for Forward Vehicle Detection with Single Camera

Pedestrian Detection Based on HOG-LBP Feature

Multiple Kernel Learning for vehicle detection in wide area motion imagery

Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos