We study the problem of action recognition from depth sequences

captured by depth cameras, where noise and occlusion are common

problems because they are captured with a single commodity camera.

In order to deal with these issues, we extract semi-local features

called random occupancy pattern (ROP) features, which employ a novel

sampling scheme that effectively explores an extremely large sampling

space. We also utilize a sparse coding approach to robustly encode these

features. The proposed approach does not require careful parameter tuning.

Its training is very fast due to the use of the high-dimensional integral

image, and it is robust to the occlusions. Our technique is evaluated on

two datasets captured by commodity depth cameras: an action dataset

and a hand gesture dataset. Our classification results are superior to

those obtained by the state of the art approaches on both datasets.

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In  12th European Conference on Computer Vision (ECCV)

Publisher  Springer
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