relation: https://khub.utp.edu.my/scholars/8980/
title: Enhanced people counting system based head-shoulder detection in dense crowd scenario
creator: Hassan, M.A.
creator: Pardiansyah, I.
creator: Malik, A.S.
creator: Faye, I.
creator: Rasheed, W.
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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2017
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011965439&doi=10.1109%2fICIAS.2016.7824053&partnerID=40&md5=6590c7d9706ff744c67ab45453d9ba90
relation: 10.1109/ICIAS.2016.7824053
identifier: 10.1109/ICIAS.2016.7824053