Robust classification of human actions from 3D data

We address the problem of classifying human actions using a single depth sensor camera. In this work, we propose an angular representation to model the relationship between the joints in human skeleton. This representation helps cope with noisy data while enhances both computational efficiency and flexibility. Also, we propose to use Hidden Markov Model (HMM) to recognize temporal motion patterns. The full skeleton formulated in a 60D feature vector is tuned to a 37D feature vector of the most active joints. These features are then fed to the HMM for recognition. We evaluate our classifier on a dataset of 19 classes and 5 indoor scenarios with hundreds of action instances recorded using the Microsoft XBOX Kinect¹ sensor and achieve an average precision/recall of 91.14%/96.89%.