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dc.contributor.authorWu, Jianxin
dc.contributor.authorGeyer, Christopher
dc.contributor.authorRehg, James M.
dc.date.accessioned2012-09-20T16:43:36Z
dc.date.available2012-09-20T16:43:36Z
dc.date.issued2011-05
dc.identifier.citationWu, J.; Geyer, C.; & Rehg, J.M. (2011). “Real-Time Human Detection Using Contour Cues". Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2011), 9-13 May 2011, pp.860-867.en_US
dc.identifier.issn1050-4729
dc.identifier.urihttp://hdl.handle.net/1853/44693
dc.description©2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_US
dc.descriptionPresented at the 2011 IEEE International Conference on Robotics and Automation (ICRA), 9-13 May 2011, Shanghai, China.
dc.descriptionDOI: 10.1109/ICRA.2011.5980437
dc.description.abstractA real-time and accurate human detector, C⁴, is proposed in this paper. C⁴ achieves 20 fps speed and stateof- the-art detection accuracy, using only one processing thread without resorting to special hardwares like GPU. Real-time accurate human detection is made possible by two contributions. First, we show that contour is exactly what we should capture and signs of comparisons among neighboring pixels are the key information to capture contours. Second, we show that the CENTRIST visual descriptor is particularly suitable for human detection, because it encodes the sign information and can implicitly represent the global contour. When CENTRIST and linear classifier are used, we propose a computational method that does not need to explicitly generate feature vectors. It involves no image pre-processing or feature vector normalization, and only requires O(1) steps to test an image patch. C⁴ is also friendly to further hardware acceleration. In a robot with embedded 1.2GHz CPU, we also achieved accurate and 20 fps high speed human detection.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectCENTRISTen_US
dc.subjectContoursen_US
dc.subjectHuman detectionen_US
dc.subjectVisual descriptoren_US
dc.titleReal-Time Human Detection Using Contour Cuesen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computing
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatenameiRobot Corporation
dc.contributor.corporatenameNanyang Technological University. School of Computer Engineering
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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