NON-NEGATIVE SPARSE CODING FOR HUMAN ACTION RECOGNITION

被引：0
|
作者：
Amiri, S. Mohsen [1 ]
Nasiopoulos, Panos [1 ]
Leung, Victor C. M. [1 ]
机构：
[1] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V5Z 1M9, Canada
来源：
2012 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2012) | 2012年
关键词：
Human Action Recognition; Computer Vision; Machine Learning; Smart Home; SVM;
D O I：
暂无
中图分类号：
TB8 [摄影技术];
学科分类号：
0804 ;
摘要：
We consider the problem of human action recognition using non-negative sparse representation of extracted features from spatiotemporal video patches. Our algorithm trains dictionaries for the calculation of a non-negative sparse representation for feature vectors and uses a linear Support Vector Machine (SVM) to distinguish between different actions. We evaluate the performance of the proposed techniques by using two human action datasets (KTH and IXMAS). In both cases, the proposed technique outperforms state-of-the-art techniques, achieving 100% accuracy on the KTH dataset.
引用
收藏
页码：1421 / 1424
页数：4
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