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Fast stereo-based pedestrian detection using hypotheses
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dc.contributor.author Kang, Min-Sung -
dc.contributor.author Lim, Young-Chul -
dc.date.available 2017-07-11T07:41:41Z -
dc.date.created 2017-05-08 -
dc.date.issued 2015-10 -
dc.identifier.isbn 9781450337380 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/3723 -
dc.description.abstract In this paper, we present a multiple hypotheses framework to detect pedestrians accurately and precisely. The multiple hypotheses framework consists of obstacle detection, pedestrian recognition and data association. Obstacle detection detects all obstacles on the road. Pedestrian recognition classifies the detected obstacles as persons or non-persons. The data association component assigns multiple results to the correct hypotheses with multiple similarity functions. The experimental results demonstrate that the proposed method enhances the accuracy and precision of the region of interest. © 2015 ACM. -
dc.language English -
dc.publisher Association for Computing Machinery, Inc -
dc.relation.ispartof RACS '15: Proceedings of the 2015 Conference on research in adaptive and convergent systems -
dc.title Fast stereo-based pedestrian detection using hypotheses -
dc.type Conference Paper -
dc.identifier.doi 10.1145/2811411.2811486 -
dc.identifier.scopusid 2-s2.0-84960857316 -
dc.identifier.bibliographicCitation Kang, Min-Sung. (2015-10). Fast stereo-based pedestrian detection using hypotheses. Research in Adaptive and Convergent Systems, RACS 2015, 131–135. doi: 10.1145/2811411.2811486 -
dc.citation.conferenceDate 2015-10-09 -
dc.citation.conferencePlace CS -
dc.citation.conferencePlace Prague -
dc.citation.endPage 135 -
dc.citation.startPage 131 -
dc.citation.title Research in Adaptive and Convergent Systems, RACS 2015 -
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Lim, Young Chul임영철

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