Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162531
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dc.contributor.authorTang, Haihanen_US
dc.date.accessioned2022-10-26T23:38:10Z-
dc.date.available2022-10-26T23:38:10Z-
dc.date.issued2022-
dc.identifier.citationTang, H. (2022). Object counting using machine learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162531en_US
dc.identifier.urihttps://hdl.handle.net/10356/162531-
dc.description.abstractIn this thesis, to improve the accuracy of multi-modal crowd count estimation, a three-stream adaptive fusion network (TAFNet) and a scale-aware self-differential attention network (SDANet) are proposed. The proposed TAFNet is adopted to adaptively extract and fuse the optical information with thermal information, increasing the effectiveness of multi-modal information fusing. The proposed SDANet utilizes multi-scale features to estimate the density map and predict crowd number, which solves the scale variation problem of crowds. Several novel modules are proposed to highlight the scale information and avoid information redundancy. The experiments on RGBT-CC benchmark show the effectiveness of proposed methods for RGB-T crowd counting compared with state-of-the-art methods. The experiments on ShanghaitechRGBD benchmark demonstrate that proposed networks are capable of RGB-D crowd counting. In addition, the estimated density maps have high quality and are close to the ground truth density maps.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Image processing and computer visionen_US
dc.titleObject counting using machine learningen_US
dc.typeThesis-Master by Researchen_US
dc.contributor.supervisorLin Zhipingen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Engineeringen_US
dc.identifier.doi10.32657/10356/162531-
dc.contributor.supervisoremailEZPLin@ntu.edu.sgen_US
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