Title:
C⁴ : A Real-time Object Detection Framework
C⁴ : A Real-time Object Detection Framework
| dc.contributor.author | Wu, Jianxin | |
| dc.contributor.author | Liu, Nini | |
| dc.contributor.author | Geyer, Christopher | |
| dc.contributor.author | Rehg, James M. | |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Interactive Computing | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. Center for Robotics and Intelligent Machines | en_US |
| dc.contributor.corporatename | Nanjing University | en_US |
| dc.contributor.corporatename | Nanyang Technological University. School of Computer Engineering | en_US |
| dc.contributor.corporatename | iRobot Corporation | en_US |
| dc.date.accessioned | 2014-04-25T13:58:53Z | |
| dc.date.available | 2014-04-25T13:58:53Z | |
| dc.date.issued | 2013-10 | |
| 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.description | DOI: 10.1109/TIP.2013.2270111 | |
| dc.description.abstract | A 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.embargo.terms | null | en_US |
| dc.identifier.citation | Wu, 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.doi | 10.1109/TIP.2013.2270111 | |
| dc.identifier.uri | http://hdl.handle.net/1853/51637 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.subject | CENTRIST | en_US |
| dc.subject | Object detection | en_US |
| dc.subject | Real-time | en_US |
| dc.title | C⁴ : A Real-time Object Detection Framework | en_US |
| dc.type | Text | |
| dc.type.genre | Article | |
| dspace.entity.type | Publication | |
| local.contributor.author | Rehg, James M. | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| relation.isAuthorOfPublication | af5b46ec-ffe2-4ce4-8722-1373c9b74a37 | |
| relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf |