Range-Sample Depth Feature for Action Recognition

We propose binary range-sample feature in depth. It is based on τ 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. W...

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Bibliographic Details
Published in2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 772 - 779
Main Authors Lu, Cewu, Jia, Jiaya, Tang, Chi-Keung
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Accuracy
Action Recognition
Algorithms
Benchmarking
Binary Feature
Computer vision
Conferences
Depth
Hamming distance
High speed
Histograms
Joints
Learning
Pattern recognition
Robustness
Running
Sampling
Three-dimensional displays
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Summary:We propose binary range-sample feature in depth. It is based on τ 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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:1063-6919
1063-6919
2575-7075
DOI:10.1109/CVPR.2014.104