eprintid: 8980
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/89/80
datestamp: 2023-11-09 16:20:55
lastmod: 2023-11-09 16:20:55
status_changed: 2023-11-09 16:13:59
type: conference_item
metadata_visibility: show
creators_name: Hassan, M.A.
creators_name: Pardiansyah, I.
creators_name: Malik, A.S.
creators_name: Faye, I.
creators_name: Rasheed, W.
title: Enhanced people counting system based head-shoulder detection in dense crowd scenario
ispublished: pub
keywords: Graphic methods; Image retrieval; Image segmentation; Support vector machines, Crowd density; Histogram of oriented gradients; Local binary patterns; People counting; Video surveillance, Security systems
note: cited By 8; Conference of 6th International Conference on Intelligent and Advanced Systems, ICIAS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125970
abstract: 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.
date: 2017
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011965439&doi=10.1109%2fICIAS.2016.7824053&partnerID=40&md5=6590c7d9706ff744c67ab45453d9ba90
id_number: 10.1109/ICIAS.2016.7824053
full_text_status: none
publication: International Conference on Intelligent and Advanced Systems, ICIAS 2016
refereed: TRUE
isbn: 9781509008452
citation:   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.  In: UNSPECIFIED.