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INTERACTING WITH SIMULATION ENVIRONMENTS THROUGH THE KINECT Fayez Alazmi Supervisor: Dr. Brett Wilkinson Flinders University Image 1Image 2Image 3 Source.

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Presentation on theme: "INTERACTING WITH SIMULATION ENVIRONMENTS THROUGH THE KINECT Fayez Alazmi Supervisor: Dr. Brett Wilkinson Flinders University Image 1Image 2Image 3 Source."— Presentation transcript:

1 INTERACTING WITH SIMULATION ENVIRONMENTS THROUGH THE KINECT Fayez Alazmi Supervisor: Dr. Brett Wilkinson Flinders University Image 1Image 2Image 3 Source : 1

2 SPECIFIC USE Flinders University Update the conventional systems in educating the fresh soldiers with Kinect based systems Develop a thorough understanding of the training field via simulation prior to entering the training field Flinders University

3 MULTIPURPOSE RANGE COMPLEX (MPRC) 133 TARGET  Tank set at different positions with the motion of hand  Can be controlled by either hand  Different gestures cause different actions.  The tank can fire a set of targets when the person speaks  Uses the Kinect array microphone

4 20 BATTLE POSITION IN MPRC

5 80 INVENTORY TARGET

6 PROJECT AIM Develop hardware and software that provides for the control of different objects in simulation via motion gestures and speech recognition. Implement a simulation environment to deploy the Kinect based system Deploy the application to a real world scenario such as battle field training (MPRC). Flinders University

7 W HAT IS THE K INECT  Developed by Microsoft  Infrared projector and RGB camera  Microphone array  3D depth sensors  Complete hands free interaction Flinders University

8 BACKGROUND  Extensive research done on depth based sensors  Has the potential to be used in mapping applications  Hand gestures operates robustly in uncontrolled environments and is insensitive to hand variations and distortions See References [1,2,3,4,5] Flinders University

9 T ECHNOLOGY  Hardware  Microsoft XBOX-360 Kinect  Monitor display/ Projector display  Standard computer/PC  Software  Microsoft Visual Studio 2012  Kinect SDK v1.7 for Windows  Windows 7 Flinders University

10 DEPTH INFORMATION  Use an IR projector to project IR pattern on the scene  Use a hardwired IR pattern  Use IR camera to see the IR pattern  Infer depth from the projected IR pattern and hardwired pattern

11 IDENTIFY A PERSON IN THE ENVIRONMENT  Based on the fact that human is usually the largest moving object in the scene.  Based on the depth data of Kinect camera  Separate the person depth data from the surrounding data  Assign 20 joint positions

12 DIFFERENTIATE BETWEEN PERSONS  Able to differentiate between two or more persons.  Can track up to six people  Identify and memorize skeletal features and use it for tracking  Respond to one specific person  Based on skeletal features of the person

13 SIMULATION EXAMPLE  The system is able to identify different hand gestures  Takes different actions according to the gestures  Take action on the targets by saying its name

14 CONCLUSION Flinders University  Implement a functional simulation environment.  Interact through motion gestures  Develop an understanding of the Kinect SDK and middleware applications for Natural Interactions  Conduct user evaluations

15 FUTURE WORK  Investigate into the possibilities of Kinect based applications  Make use of this system relevant to my field; ultimate goal is to assist in the training access.  Determine stability of low cost technology for such systems. Flinders University

16 REFERENCES  [1]. Thomas B Moeslund, Adrian Hilton, and Volker Kru. A Survey of Advances in Visionbased Human Motion Capture and Analysis. Computer Vision and Image Understanding, 104:90–126,  [2]. B. Freedman, A. Shpunt, M. Machline, and Y. Arieli. Depth Mapping Using Projected Patterns. Patent Application, WO 2008/ A2.  [3]. Mark Schneider and Charles Stevens. Development and Testing of a New Magnetictracking Device for Image Guidance. SPIE Medical Imaging, pages 65090I–65090I– 11,  [4]. Ali Erol, George Bebis, Mircea Nicolescu, Richard D. Boyle, and Xander Twombly. Vision-based Hand Pose Estimation: A review. Computer Vision and Image Understanding, 108(1-2):52–73,  [5]. Iason Oikonomidis, Nikolaos Kyriazis, and Antonis A. Argyros. Markerless and Efficient 26-DOF Hand Pose Recovery. ACCV, pages 744–757, Flinders University


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