dc.contributor.author | Khan, Sultan Daud | |
dc.contributor.author | Ullah, Habib | |
dc.contributor.author | Uzair, Mohammad | |
dc.contributor.author | Ullah, Mohib | |
dc.contributor.author | Ullah, Rehan | |
dc.contributor.author | Alaya Cheikh, Faouzi | |
dc.date.accessioned | 2020-02-10T15:30:52Z | |
dc.date.available | 2020-02-10T15:30:52Z | |
dc.date.created | 2020-01-21T12:47:42Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Proceedings of IEEE international conference on image processing. 2019, 2019-September 4474-4478. | nb_NO |
dc.identifier.issn | 1522-4880 | |
dc.identifier.uri | http://hdl.handle.net/11250/2640870 | |
dc.description.abstract | People counting in high density crowds is emerging as a new frontier in crowd video surveillance. Crowd counting in high density crowds encounters many challenges, such as severe occlusions, few pixels per head, and large variations in person's head sizes. In this paper, we propose a novel Density Independent and Scale Aware model (DISAM), which works as a head detector and takes into account the scale variations of heads in images. Our model is based on the intuition that head is the only visible part in high density crowds. In order to deal with different scales, unlike off-the-shelf Convolutional Neural Network (CNN) based object detectors which use general object proposals as inputs to CNN, we generate scale aware head proposals based on scale map. Scale aware proposals are then fed to the CNN and it renders a response matrix consisting of probabilities of heads. We then explore non-maximal suppression to get the accurate head positions. We conduct comprehensive experiments on two benchmark datasets and compare the performance with other state-of-theart methods. Our experiments show that the proposed DISAM outperforms the compared methods in both frame-level and pixel-level comparisons. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | nb_NO |
dc.title | Disam: Density Independent and Scale Aware Model for Crowd Counting and Localization | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 4474-4478 | nb_NO |
dc.source.volume | 2019-September | nb_NO |
dc.source.journal | Proceedings of IEEE international conference on image processing | nb_NO |
dc.identifier.doi | 10.1109/ICIP.2019.8803409 | |
dc.identifier.cristin | 1779149 | |
dc.description.localcode | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |