A Kinect Based Immersive Video Conferencing System

This paper presents a Kinect Based Immersive Video Conferencing System (KIVC), which aims at improving the quality of user experience (QoE) in the future video conferencing communication. In our KIVC system, we choose Kinect as the camera for capturing the videos, benefiting from the state-of-the-art computer vision toolbox integrated in Kinect. In addition, in order to improve QoE with the constraint on the limited bandwidth, we develop a framework for KIVC system. Then, in our KIVC system, several advanced key technologies are adopted, e.g., fast object-based video coding for real-time data, human-computer interaction, and blurring processing for attracting human visual attention. Extensive experimental results show that our KIVC system has excellent performance and achieves a reliable real-time processing results. As such, the QoE can be enhanced with plenty of user-customized applications by KIVC.

[1]  Simon Lucey,et al.  Face alignment through subspace constrained mean-shifts , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[2]  Reinhard Klette,et al.  Moving Object Segmentation Using Optical Flow and Depth Information , 2009, PSIVT.

[3]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[4]  Zhengyou Zhang,et al.  Microsoft Kinect Sensor and Its Effect , 2012, IEEE Multim..

[5]  Yanjiang Wang,et al.  An improved adaptive background modeling algorithm based on Gaussian Mixture Model , 2008, 2008 9th International Conference on Signal Processing.

[6]  Shengkui Zhao,et al.  ITEM: Immersive Telepresence for Entertainment and Meetings—A Practical Approach , 2014, IEEE Journal of Selected Topics in Signal Processing.

[7]  Z. Zivkovic Improved adaptive Gaussian mixture model for background subtraction , 2004, ICPR 2004.

[8]  G. Song,et al.  Corrosion behaviour of AZ21, AZ501 and AZ91 in sodium chloride , 1998 .

[9]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[10]  R. Hetherington The Perception of the Visual World , 1952 .

[11]  J. Gibson The perception of the visual world , 1951 .

[12]  Guillaume-Alexandre Bilodeau,et al.  A Multiscale Region-Based Motion Detection and Background Subtraction Algorithm , 2010, Sensors.

[13]  Jana Abhijit Kinect for Windows SDK Programming Guide , 2012 .

[14]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[15]  K. Kuroda,et al.  A precise and fast temperature mapping using water proton chemical shift , 1995, Magnetic resonance in medicine.

[16]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[17]  Jaya Shukla,et al.  A Method for Hand Gesture Recognition , 2014, 2014 Fourth International Conference on Communication Systems and Network Technologies.

[18]  Yogananda Patnaik,et al.  Region of interest based scalable image and video coding a superlative study , 2014, 2014 2nd International Conference on Emerging Technology Trends in Electronics, Communication and Networking.

[19]  Ton Kalker,et al.  The Road to Immersive Communication , 2012, Proceedings of the IEEE.

[20]  Ying Chang,et al.  The Network Architecture and Schematization of Henan Electric Power Video-conference System , 2014 .