Upper Pose Estimation Based On Muti-features OAR Human Model In Static Images

Human pose estimation in static images is an important research topic in pattern recognition and the basis of many computer vision systems. Although there is lots of work in this field and some progresses have been made recently, recovering different poses in various backgrounds and clothing is still very challenging. To solve this problem, a novel upper pose estimation algorithm based on MF-OAR human model is proposed in this thesis, which includes image feature extraction, joints candidates detection, human pose constrain description, and pose probability model design. The main contributions are listed as follows.Firstly, a skin segmentation algorithm based on Graph cuts is proposed. It considers the skin color, background color and pixel location during the skin detection stage, with the skin color and background color being modeled dynamically based on the detected face. Compared with existing methods, it generates more accurate and complete skin regions for pose estimation.Secondly, a filter based on probability field is proposed to solve the smallest cut in the image segmentation methods based on Graph cuts. It improves the robustness of image segmentation methods against backgrounds and illuminations by enhancing the stability of foreground and background probability distributions. Implementations of such a filter in both skin region and clothing segmentations show that it improves the segmentation results greatly.Thirdly, most existing part detection methods find the human parts by using only one image feature, which produces a lot of poor part candidates. To solve this problem, a joints initialization method based on MF-OAR human model is proposed to get more accurate joints candidates. It firstly divides the pose space into three sub-spaces, and then each joint candidate is found within the corresponding sub-space to reduce the computation cost. Furthermore, it keeps up to three reliable candidates for each joint by integrating multiple image features (edge, foreground and background color, skin regions), which simplifies the pose inferring problem and gets more accurate pose estimation.Fourthly, joints initialization and pose estimation are both the problems of posterior probability maximization, where the key is selecting reasonable likelihood functions. In our method, the following likelihood functions are designed to improve the pose estimation capability: 1) the improved oriented Chamfer matching makes the edge likelihood more robust against background, clothing and illumination; 2) the foreground and background likelihood functions based on the probability field are proposed to determine whether each pose candidate is in the foreground or the background; 3) both the skin likelihood and region likelihood are designed by using the skin regions to help locating the arm during the pose inferring.Experiments on both the USC people database and 440 our collected images show that our method can recover upper body poses from images with a variety of individuals, poses, backgrounds and clothing more accurately than existing methods, with the success rate of 62.1% in 440 images.