Range-Sample Depth Feature for Action Recognition

Cewu Lu, Jiaya Jia, Chi-Keung Tang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 772-779

Abstract


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-theart results on benchmark datasets in our experiments. Impressively short running time is also yielded.

Related Material


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[bibtex]
@InProceedings{Lu_2014_CVPR,
author = {Lu, Cewu and Jia, Jiaya and Tang, Chi-Keung},
title = {Range-Sample Depth Feature for Action Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}