DC Field | Value | Language |
---|---|---|
dc.contributor.author | Khan, Asim | ko |
dc.contributor.author | Kyung, Chong-Min | ko |
dc.date.accessioned | 2020-12-21T02:10:13Z | - |
dc.date.available | 2020-12-21T02:10:13Z | - |
dc.date.created | 2020-12-11 | - |
dc.date.issued | 2016-10-25 | - |
dc.identifier.citation | 13th International SoC Design Conference, ISOCC 2016, pp.127 - 128 | - |
dc.identifier.issn | 2163-9612 | - |
dc.identifier.uri | http://hdl.handle.net/10203/278803 | - |
dc.description.abstract | Pedestrian detection being a vital as well as complex problem poses a unique challenge from accuracy and complexity point of view. On-chip memory requirement is one of the key issues for sliding window based detectors. In this paper a memory efficient hardware architecture is proposed which estimates the weights from a partially stored model at runtime. It uses a simple and robust feature with histogram intersection classifier. The implementation results show 80% reduction in logic resources and 46% reduction in memory without sacrificing accuracy as compared to the state of the art hardware implementations. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Memory efficient hardware accelerator for kernel support vector machine based pedestrian detection | - |
dc.type | Conference | - |
dc.identifier.wosid | 000392251200063 | - |
dc.identifier.scopusid | 2-s2.0-85010407939 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 127 | - |
dc.citation.endingpage | 128 | - |
dc.citation.publicationname | 13th International SoC Design Conference, ISOCC 2016 | - |
dc.identifier.conferencecountry | KO | - |
dc.identifier.conferencelocation | Ramada Plaza Jeju Hotel | - |
dc.identifier.doi | 10.1109/ISOCC.2016.7799723 | - |
dc.contributor.localauthor | Kyung, Chong-Min | - |
dc.contributor.nonIdAuthor | Khan, Asim | - |
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