Full-body person recognition system.
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. © 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
|Title:||Full-body person recognition system|
|Keywords:||Multi-class classification, Object recognition, Pattern classification, Person recognition, Support vector machines, Surveillance systems|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
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