Human Activity Recognition Using Spatiotemporal 3 D Body Joint Features with Hidden Markov Models
Video based human activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart home healthcare, and human computer interactions. The essential goals of a video based activity recognition system include the provision of behavior based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human activity recognition, whereby human body joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained activity HMMs are used for depth video activity recognition.
KSII Trans. Internet Inf. Syst.