Robust person recognition using CNN
dc.contributor.author | Chen, M. | |
dc.contributor.author | Lin, Q. | |
dc.contributor.author | Allebach, J. | |
dc.contributor.author | Zhu, Maggie | |
dc.date.accessioned | 2018-08-08T04:42:44Z | |
dc.date.available | 2018-08-08T04:42:44Z | |
dc.date.created | 2018-08-08T03:50:50Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Chen, M. and Lin, Q. and Allebach, J. and Zhu, M. 2017. Robust person recognition using CNN, pp. 45-50. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/69901 | |
dc.identifier.doi | 10.2352/ISSN.2470-1173.2017.10.IMAWM-165 | |
dc.description.abstract |
© 2017, Society for Imaging Science and Technology. Person detection and recognition has many applications in autonomous driving, smart home and smart office applications. Knowledge about the presence of a person in the environment can be used in safety solutions such as collision avoidance, in energy conservation solutions such as turning lights and air-conditioning off when there is no person around, and in meeting and collaboration solutions such as locating a vacant room. In this paper, we present a solution that can reliably detect and recognize persons under different lighting conditions and pose based on head detection and recognition using deep learning. The system is proved to achieve good results on a challenging dataset. | |
dc.title | Robust person recognition using CNN | |
dc.type | Conference Paper | |
dcterms.source.startPage | 45 | |
dcterms.source.endPage | 50 | |
dcterms.source.issn | 2470-1173 | |
dcterms.source.title | IS and T International Symposium on Electronic Imaging Science and Technology | |
dcterms.source.series | IS and T International Symposium on Electronic Imaging Science and Technology | |
curtin.department | School of Public Health | |
curtin.accessStatus | Fulltext not available |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |