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dc.contributor.authorWu, Jianxin
dc.contributor.authorLiu, Nini
dc.contributor.authorGeyer, Christopher
dc.contributor.authorRehg, James M.
dc.date.accessioned2014-04-25T13:58:53Z
dc.date.available2014-04-25T13:58:53Z
dc.date.issued2013-10
dc.identifier.citationWu, J.; Liu, N; Geyer, C.; & Rehg, J.M. (2013). : A Real-Time Object Detection Framework.” IEEE Transactions on Image Processing, Vol. 22, no.10, (October 2013), pp.4096-4107.
dc.identifier.urihttp://hdl.handle.net/1853/51637
dc.description©2013 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.descriptionDOI: 10.1109/TIP.2013.2270111
dc.description.abstractA real-time and accurate object detection framework, C⁴, is proposed in this paper. C⁴ achieves 20 fps speed and state-of-the-art detection accuracy, using only one processing thread without resorting to special hardwares like GPU. Real-time accurate object detection is made possible by two contributions. First, we conjecture (with supporting experiments) that contour is what we should capture and signs of comparisons among neighboring pixels are the key information to capture contour cues. Second, we show that the CENTRIST visual descriptor is suitable for contour based object 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 preprocessing or feature vector normalization, and only requires O(1) steps to test an image patch. C⁴ is also friendly to further hardware acceleration. It has been applied to detect objects such as pedestrians, faces, and cars on benchmark datasets. It has comparable detection accuracy with state-of-the-art methods, and has a clear advantage in detection speed.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectCENTRISTen_US
dc.subjectObject detectionen_US
dc.subjectReal-timeen_US
dc.titleC⁴ : A Real-time Object Detection Frameworken_US
dc.typeArticle
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameNanjing Universityen_US
dc.contributor.corporatenameNanyang Technological University. School of Computer Engineeringen_US
dc.contributor.corporatenameiRobot Corporationen_US
dc.identifier.doi10.1109/TIP.2013.2270111
dc.embargo.termsnullen_US


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