Cover Image

Enhanced people counting system based head-shoulder detection in dense crowd scenario

Counting precisely the number of people in a crowd is one of the most attractive issues for video analytics application. In this paper, an integrated method using Histogram of Oriented Gradient (HOG) and Completed Local Binary Pattern (CLBP) is proposed to detect a head-shoulder region of people wit...

Full description

Saved in:
Bibliographic Details
Main Authors: Hassan, M.A., Pardiansyah, I., Malik, A.S., Faye, I., Rasheed, W.
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011965439&doi=10.1109%2fICIAS.2016.7824053&partnerID=40&md5=6590c7d9706ff744c67ab45453d9ba90
http://eprints.utp.edu.my/20188/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utp.eprints.20188
record_format eprints
spelling my.utp.eprints.201882018-04-22T14:44:57Z Enhanced people counting system based head-shoulder detection in dense crowd scenario Hassan, M.A. Pardiansyah, I. Malik, A.S. Faye, I. Rasheed, W. Counting precisely the number of people in a crowd is one of the most attractive issues for video analytics application. In this paper, an integrated method using Histogram of Oriented Gradient (HOG) and Completed Local Binary Pattern (CLBP) is proposed to detect a head-shoulder region of people within image or video sequence. Head-shoulder region is used as features to detect people against the false positive and false negative issue. HOG and CLBP are used to extract the edge contour and texture features of head-shoulder region, respectively. The two features are fused together to generate a combined feature vector. Support Vector Machine (SVM) is used to execute classification of the fusion features to classify people from a mixture of objects. The results show that the detection rate of the proposed method HOG-CLBP, on Recall value and Accuracy, achieves better performance compared to the current method for dense crowd scenario. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011965439&doi=10.1109%2fICIAS.2016.7824053&partnerID=40&md5=6590c7d9706ff744c67ab45453d9ba90 Hassan, M.A. and Pardiansyah, I. and Malik, A.S. and Faye, I. and Rasheed, W. (2017) Enhanced people counting system based head-shoulder detection in dense crowd scenario. International Conference on Intelligent and Advanced Systems, ICIAS 2016 . http://eprints.utp.edu.my/20188/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Counting precisely the number of people in a crowd is one of the most attractive issues for video analytics application. In this paper, an integrated method using Histogram of Oriented Gradient (HOG) and Completed Local Binary Pattern (CLBP) is proposed to detect a head-shoulder region of people within image or video sequence. Head-shoulder region is used as features to detect people against the false positive and false negative issue. HOG and CLBP are used to extract the edge contour and texture features of head-shoulder region, respectively. The two features are fused together to generate a combined feature vector. Support Vector Machine (SVM) is used to execute classification of the fusion features to classify people from a mixture of objects. The results show that the detection rate of the proposed method HOG-CLBP, on Recall value and Accuracy, achieves better performance compared to the current method for dense crowd scenario. © 2016 IEEE.
format Article
author Hassan, M.A.
Pardiansyah, I.
Malik, A.S.
Faye, I.
Rasheed, W.
spellingShingle Hassan, M.A.
Pardiansyah, I.
Malik, A.S.
Faye, I.
Rasheed, W.
Enhanced people counting system based head-shoulder detection in dense crowd scenario
author_facet Hassan, M.A.
Pardiansyah, I.
Malik, A.S.
Faye, I.
Rasheed, W.
author_sort Hassan, M.A.
title Enhanced people counting system based head-shoulder detection in dense crowd scenario
title_short Enhanced people counting system based head-shoulder detection in dense crowd scenario
title_full Enhanced people counting system based head-shoulder detection in dense crowd scenario
title_fullStr Enhanced people counting system based head-shoulder detection in dense crowd scenario
title_full_unstemmed Enhanced people counting system based head-shoulder detection in dense crowd scenario
title_sort enhanced people counting system based head-shoulder detection in dense crowd scenario
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011965439&doi=10.1109%2fICIAS.2016.7824053&partnerID=40&md5=6590c7d9706ff744c67ab45453d9ba90
http://eprints.utp.edu.my/20188/
_version_ 1738656176119218176
score 13.211869

Similar Items