1. Poster Spotlights Session 1A: Thursday Morning, June 11th Articulated Gaussian Kernel Correlation for Human Pose Estimation Meng Ding and Guoliang Fan, School of Electrical and Computer Engineering Oklahoma State University

2. Articulated Gaussian Kernel Correlation for Human Pose Estimation Motivation –Kinect triggers lots of research for human motion capture (Mocap). Human motion analysis Human computer interaction (HCI) 3D computer animation Challenges –High computational complexity –Large database for querying or training Our Goals –Without querying database or training data –A general articulated pose tracking framework –Robust to noisy depth data P. Kohli, J. Shotton. "Key developments in human pose estimation for kinect." Consumer Depth Cameras for Computer Vision. Springer London, 2013. 63-70. A. Baak, et al. "A data-driven approach for real-time full body pose reconstruction from a depth camera.“, ICCV, 2011. https://www.microsoft.com/en-us/kinectforwindows/meetkinect/default.aspx

3. Approaches –Both the human body template and the observed 3D point cloud are represented by Gaussian kernels. –Pose estimation is treated as maximizing their Gaussian kernel correlation (KC). Contributions –A unified Gaussian KC function –An articulated Gaussian KC embedded with a kinematic skeleton –A real-time and robust articulated pose tracking framework. Articulated Gaussian Kernel Correlation for Human Pose Estimation

4. Experimental Results (SMMC-10) Articulated Gaussian Kernel Correlation for Human Pose Estimation