Abstract
We propose a real-time system to recognize human upper body posture and predict positions of upper limbs joints. The working environment of the system is for the user sitting in front of the computer, and the camera is set up at the top of the screen. We consider users interact with the system in front of the computer, the camera is only available to get the human upper body images. The system input is a real-time depth image, captured by using Microsoft depth camera Kinect. The system has two outputs: The user's action (Normal sitting position, raised his left hand, raised his right hand, raised his hands) and estimated location of body parts (Face, shoulders, arms, elbows, palms, body, etc). FPS of the whole system between about 14 to 18, varies due to the total required processing pixels. System architecture can be divided into two phases: In the first stage, Depth images after the pre-processing and feature extraction (Depth Context), are analyzed by the action classifier (Random Forest [16]) to Identify the current user action type. Then, the time dependency is applied to correct action type. In the second stage, according to the user action type we select an appropriate body classifier (Pixel based Random Forest), to classify pre-processing depth image and identify the distribution of body parts. Later, considering the time dependency of each body part, we correct the overlapping body part, and determine the estimated positions of body parts.