Abstract
Recently, estimating human poses from a monocular view has broad applications in human-computer interface, virtual reality and video surveillance. In this thesis, we present an example-based approach for 3D human body pose estimation from a static silhouette image. To improve the estimating accuracy of the occluded body parts, we use Microsoft kinect to capture the depth image of human model as our example features. Our main idea of estimation is that if the contour of the input human image is similar with the contour of the model in database, the pose parameters are considered to be the same. And to reduce the estimation time, we apply the Locality-Sensitive Hashing to index the global parameter of the human poseFirst, we use the background model constructed by depth image to apply background subtraction and get the human silhouette. This helps to reduce the effect of light and shadow. After several morphological processes, we can get the human contour image. Then we use the shape context of the patches extracted from the contour image as the feature vector. After training state, we get a useful hash function to encode each feature vector into a hash value which is an index number for hash table construction. A useful hash function supposed to encode similar patches as same hash value and different patches as different hash values. At testing state, according to the hash value of input patch, we retrieve similar patches from the hash table and apply the Hough voting algorithm. After temporal and prediction constraint, the pose parameters with the highest voting are considered to be our estimation result.