Human behavior recognition based on 3D features and hidden markov models

Human vision system can receive the RGB and depth information at the same time and make an accurate judgment on human behaviors. However, in an ordinary camera, there is a loss in information when a 3D image is projected to a 2D plane. The depth and RGB information collected simultaneously by Kinect can provide more discriminant information for human behaviors than traditional cameras. Therefore, RGB-D camera is thought to be the key of solving human behavior recognition for a long time. In this paper, we develop 3D motion scale invariant feature transform for the description of the depth and motion information. It serves as a more effective descriptor for the RGB and depth videos. Hidden Markov Model is utilized for improving the accuracy of human behavior recognition. Experiments show that our framework provides richer information for discriminative point of behavior analysis and obtains better recognition performance.