Abstract
This thesis proposes a real-time system to recognize human upper body posture and predict positions of upper limbs joints using the depth image captured by using Kinect. The system consists of three stages: (1) action recognition, (2) body part segmentation, (3) offset compensation. In the 1st stage, the depth images after the pre-processing and feature extraction are analyzed by the action type classifier to identify the current user action type. Then, the temporal correlation between the recognized action types can be applied for action type correction. In the 2nd stage, based on the user action type, we select an appropriate body classifier to classify pre-processing depth image and identify the distribution of body part. We also consider the time dependency and correlation of each body part to solve the occlusion problem of body part. In the 3rd stage, we develop the offset classifiers based on the difference between the output of the 2nd stage and the ground truth. For different user action type, we select an appropriate offset classifier to find the offset and compensate the output of the body classifier. Based on the results of before and after offset compensation, and depth silhouette, we can determine this information to identify which result is better as the final output of body location.