|
|||||
|
|
Full-body person recognition system |
|
| Authors: | Chikahito NakajimaAuthor Vitae Bernd HeiseleAuthor VitaeTomaso PoggioAuthor Vitae |
| Affiliation: | a Central Research Institute of Electric Power Industry, 2-11-1, Iwado Kita, Komae, Tokyo 201-8511, Japan b Department of Computer Science, University College London, Gower Street, London WC1E 6BT, England, UK c Center for Biological and Computational Learning, M.I.T., 45 Carleton St., 02142, Cambridge, MA, USA |
| Abstract: | We describe a system that learns from examples to recognize persons in images taken indoors. Images of full-body persons are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine (SVM) classifiers. Different types of multi-class strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers. The experimental results show high recognition rates and indicate the strength of SVM-based classifiers to improve both generalization and run-time performance. The system works in real-time. |
| Keywords: | Multi-class classification Person recognition Pattern classification Support vector machines Surveillance systems Object recognition |
| 本文献已被 ScienceDirect 等数据库收录！ | |