Abstract
Depth cameras become popular for the gradually acceptable price and their capability to solve relatively difficult problems such as object detection [1], pose tracking [2], and particularly the successful pose recognition system developed by Shotton et al. [3]. In this thesis we use the depth information to develop our application. We obtain depth images and human skeleton coordinates using Kinect and we focus on processing the skeleton information. Our work has two goals: (1) Learning a distance metric that can identify similar-pose videos and distinguish dissimilar-pose videos. (2) Constructing a graph that describes the relation of the poses for synthesizing consecutive pose-sequence. We use multidimensional scaling (MDS) with the learned distance metric to reduce the dimensionality of poses. We present a method for describing distinct poses and for interpolating the lost frames. The reduced pose dimension also contributes to saving computation time, handling large datasets, and visualization. Our experiments show the effectiveness of our method for modeling and synthesizing pose sequences.