Human Activity Recognition as Time-Series Analysis

We propose a system that can recognize daily human activities with a Kinect-style depth camera. Our system utilizes a set of view invariant features and the hidden state conditional random field (HCRF) model to recognize human activities from the 3D body pose stream provided by MS Kinect API or OpenNI. Many high-level daily activities can be regarded as having a hierarchical structure where multiple subactivities are performed sequentially or iteratively. In order to model effectively these high-level daily activities, we utilized a multiclass HCRF model, which is a kind of probabilistic graphical models. In addition, in order to get view-invariant, but more informative features, we extract joint angles from the subject's skeleton model and then perform the feature transformation to obtain three different types of features regarding motion, structure, and hand positions. Through various experiments using two different datasets, KAD-30 and CAD-60, the high performance of our system is verified.