Modeling spatial layout of features for real world scenario RGB-D action recognition

Depth information improves skeleton detection, thus skeleton based methods are the most popular methods in RGB-D action recognition. But skeleton detection working range is limited in terms of distance and view-point. Most of the skeleton based action recognition methods ignore fact that skeleton may be missing. Local points-of-interest (POIs) do not require skeleton detection. But they fail if they cannot detect enough POIs e.g. amount of motion in action is low. Most of them ignore spatial-location of features. We cope with the above problems by employing people detector instead of skeleton detector. We propose method to encode spatial-layout of features inside bounding box. We also introduce descriptor which encodes static information for actions with low amount of motion. We validate our approach on: 3 public data-sets. The results show that our method is competitive to skeleton based methods, while requiring much simpler people detection instead of skeleton detection.