Range-Sample Depth Feature for Action Recognition

被引：75

作者：

Lu, Cewu ^{[1
]}

Jia, Jiaya ^{[2
]}

Tang, Chi-Keung ^{[1
]}

机构：

[1] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China

[2] Chinese Univ Hong Kong, Hong Kong, Peoples R China

来源：

2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2014年

关键词：

D O I：

10.1109/CVPR.2014.104

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose binary range-sample feature in depth. It is based on t tests and achieves reasonable invariance with respect to possible change in scale, viewpoint, and background. It is robust to occlusion and data corruption as well. The descriptor works in a high speed thanks to its binary property. Working together with standard learning algorithms, the proposed descriptor achieves state-of-the-art results on benchmark datasets in our experiments. Impressively short running time is also yielded.

引用

页码：772 / 779

页数：8