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dc.contributor.authorHuynh, Van-Suen_US
dc.contributor.authorVu-Hoang Tranen_US
dc.contributor.authorHuang, Ching-Chunen_US
dc.date.accessioned2020-05-05T00:01:58Z-
dc.date.available2020-05-05T00:01:58Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-7281-4569-3en_US
dc.identifier.issn1062-922Xen_US
dc.identifier.urihttp://hdl.handle.net/11536/154027-
dc.description.abstractNowadays, image-based people counting is an essential technique for public safety management. However, this work is still extremely challenging due to many kinds of scale issues caused by different congested scenes, different viewing points, different image sizes, and different density levels. In this paper, we proposed a CNNs-based framework for people counting and crowd density map estimation with the consideration of the scale problems. First, we introduced an encoder-decoder architecture, which is composed of Inception modules to learn the multi-scale feature representations. Besides, to be adaptive to image resolution, a multi-loss setting over different resolutions of density maps is designed for network training. Second, we apply multi-task learning to learn the joint features for the density map estimation task and the density level classification task. This helps to enhance the feature generality under different scenes. Finally, by adopting the U-net architecture, the encoder and decoder features are then fused to generate high-resolution density maps. The efficacy of the proposed method is evaluated in the extensive experiments by quantifying the counting performance through multiple evaluation criteria.en_US
dc.language.isoen_USen_US
dc.subjectCrowd countingen_US
dc.subjectDeep Learningen_US
dc.subjectMulti-task learningen_US
dc.subjectInception moduleen_US
dc.subjectDensity level classificationen_US
dc.titleIUML: INCEPTION U-NET BASED MULTI-TASK LEARNING FOR DENSITY LEVEL CLASSIFICATION AND CROWD DENSITY ESTIMATIONen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC)en_US
dc.citation.spage3019en_US
dc.citation.epage3024en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000521353903007en_US
dc.citation.woscount0en_US
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