Multi-image Crowd Counting Using Multi-column Convolutional Neural Network

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dc.authorid0000-0002-4595-8031en_US
dc.authorid0000-0002-9174-0367en_US
dc.contributor.authorKurnaz, Oğuzhan
dc.contributor.authorHanilçi, Cemal
dc.date.accessioned2022-10-12T07:45:55Z
dc.date.available2022-10-12T07:45:55Z
dc.date.issued2022en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.description.abstractCrowd density estimation is an important task for security applications. It is a regression problem consisting of feature extraction and estimation steps. In this study, we propose to use a modified version of previously introduced multi-column convolutional neural network (MCNN) approach for estimating crowd density. While in the original MCNN approach the same input image is applied to the each column of the network, we first propose to apply a different version of the same input image to extract a different mapping from each column. Second, original MCNN first generates an estimated density map and then performs crowd counting. Therefore, we adopt it for crowd counting and compare its performance with the proposed method. Regression task is performed by support vector regression (SVR) using feature vectors obtained from MCCNN. 2000 images selected from UCSD pedestrian dataset are used in the experiments. The regions of interest (ROI) are filtered out and the pixel values at the remaining regions are set to zero. In order to prevent distortion caused by camera position, perspective normalization has been applied as a pre-processing step which dramatically improves the performance.en_US
dc.identifier.doi10.1007/978-981-16-2380-6_20en_US
dc.identifier.endpage232en_US
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage223en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/2062
dc.identifier.volume236en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorKurnaz, Oğuzhan
dc.institutionauthorHanilçi, Cemal
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartof6th International Congress on Information and Communication Technology (ICICT)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCrowd density estimationen_US
dc.subjectConvolutional neural networken_US
dc.subjectCrowd countingen_US
dc.titleMulti-image Crowd Counting Using Multi-column Convolutional Neural Networken_US
dc.typeConference Objecten_US

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