Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3483
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dc.contributor.authorSahoo, Suraj Prakash-
dc.contributor.authorAri, Samit-
dc.date.accessioned2020-01-22T04:59:13Z-
dc.date.available2020-01-22T04:59:13Z-
dc.date.issued2020-01-
dc.identifier.citationInternational Conference on Computer, Electrical & Communication Engineering (ICCECE-2020), 17-18 January 2020, Kolkata, West Bengal, Indiaen_US
dc.identifier.urihttp://hdl.handle.net/2080/3483-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractHuman actions are challenging to recognize as it varies its shape from different angle of perception. To tackle this challenge, a multi view camera set up can be arranged, however, it is not cost effective. To handle this issue, a multi stream deep learning network is proposed in this work which is trained on different 3D projected planes. The extracted projected planes which represents different angle of perception, are used as an alternative to multi view action recognition. The projected planes are such that they represents top, side and front view for the action videos. The projected planes are then fed to a three stream deep convolutional neural network. The network uses transfer learning technique to avoid training from scratch. Finally, the scores from three streams are fused to provide the final score to recognize the query video. To evaluate the proposed work, the challenging KTH dataset is used which is widely used and publicly available. The results show that the proposed work performs better compared to state-of-the-art techniques.en_US
dc.subjectConvolutional neural networken_US
dc.subjectProjected planes, score fusionen_US
dc.subjectTransfer learningen_US
dc.titleA Three Stream Deep Network on Extracted Projected Planes for Human Action Recognitionen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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