Robust 3D Action Recognition with Random Occupancy Patterns

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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