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Introduction To Gestures
Design Of The Proposed System
Hand Detection
Hardware Implementation

Published in: Design
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  1. 1. Swarm robotics
  2. 2. PRESENTATION OVERVIEW  Introduction To Gestures  Design Of The Proposed System  Hand Detection  Hardware Implementation  Conclusion / Future Work
  3. 3. Real World Insect Examples
  4. 4. Natural swarms  Decentralised – no-one in control  Individuals are simple and autonomous  Local communication and control  Cooperative behaviours emerge through self- organisation e.g. repairing damage to nest, foraging for food, caring for brood
  5. 5. Bees
  6. 6. An In-depth Look at Real Ant Behaviour Interrupt The Flow
  7. 7. The Path Thickens! Adapting to Environment Changes
  8. 8. The New Shortest Path Adapting to Environment Changes
  9. 9. Welcome to the Real World
  10. 10. Robots • Collective task completion • No need for overly complex algorithms • Adaptable to changing environment
  11. 11. Swarm robotics  Inspired by self-organisation of social insects  Using local methods of control and communication Local control: autonomous operation Local communication: avoids bottlenecks Scalable – new robots can be added, or fail without need for recalibration Simplicity – cheap, expendable robots  Self-organisation  Decentralisation
  12. 12. Collective Robotics Swarm Robotics
  13. 13. Introduction To Gestures  Gestures can originate from any body motion.  Commonly from face/hand.  Gesture recognition-understand human body language.  Help human to interact with machines without any mechanical devices.
  14. 14. Disadvantages of centralised control and communication.  Central control: failure of controller implies failure of whole system  Robot to robot communication becomes very complex as number of robots increases.  Communication bottlenecks  Adding new robots means changing the communication and control system
  15. 15. Design Of The Proposed System  Hand detection  Feature extraction  Gesture recognition  Goal directed navigation of swarm robots.
  16. 16. Hand Detection  Hand detection – Detection of hand in an image, background objects are avoided for feature extraction. – Skin color is the key component. – Detecting skin and non-skin. – Detecting image pixels and regions that contains skin- tone color. – Background is controlled. – Appearance depends on illumination conditions.  Two phases – Training phase – Detection phase
  17. 17. Training Phase  Three steps – Collecting a database of skin patches from different images – Choosing a suitable color space – Learning the parameters of skin classifier
  18. 18. Detection Phase  Two steps Converting the image into some color space that was used in training phase. Classifying each pixel using the skin classifier to either a skin or non-skin.  RGB color space  Skin classifier Variety of classification techniques Any pixel which color falls inside the skin color class boundary is labeled as skin.
  19. 19. Feature Extraction  Feature-An interesting part of an image.  No exact definition.  Depends on the problem.  Transforming the input data into set of features.  Result is a feature vector.  Features extracted are invariant to image scaling, rotation and less affected to changes in illumination.  SIFT feature extraction.
  20. 20.  Hand tracking and Feature extraction.
  21. 21. Image frame from webcam Tracking hand by skin detection SIFT feature extraction Finding match Gesture database Gesture1 Gesture2 Gesture3 Action1 Action2 Action3 Navigation of swarm
  22. 22. Hardware Implementation  Foot-bot robot
  23. 23. Applications Of The System  Three foot-bots.
  24. 24. Applications of swarm approach Some tasks are particularly suited to group of expendable simple robots e.g. - cleaning up toxic waste - exploring an unknown planet - pushing large objects - surveillance and other military applications
  25. 25. Conclusion  Hand detection and feature extraction removes noise from the image.  System performance and accuracy will increase.  Swarm robots movement can be controlled through gestures. Future Work Speech recognition.
  26. 26. Dumb parts, properly connected into a swarm, yield smart results. Kevin Kelly
  27. 27. Satellite Maintenance The Future? Medical Interacting Chips in Mundane Objects Cleaning Ship Hulls Pipe Inspection Pest Eradication M iniaturization EngineMaintenance Telecommunications Self-Assem bling Robots Job Scheduling Vehicle Routing Data Clustering Distributed M ail System s O ptim al Resource Allocation Combinatorial Optimization
  28. 28. 1. C.C.Wang, K.C.Wang.: Hand Posture Recognition Using Adaboost with SIFT For Human Robot Interaction, in Robotics: Viable Robotic Service to Human, Springer-2009. 2. M. Kolsch and M. Turk.: Robust hand detection, in IEEE International Conference on Automatic Face and Gesture Recognition, 2004. 3. Cristina Manresa, Javier Varona, Ramon Mas and Francisco J.Perales,.: Hand Tracking and Gesture Recognition for Human-Computer Interaction , Electronic Letters on Computer Vision and Image Analysis 5(3):96-104, 2005. 4. Alessandro Giusti, Jawad Nagi, Luca M. Gambardella, Gianni A. Di Caro : Distributed Consensus for Interaction between Humans and Mobile Robot Swarms (Demonstration). 5. Ihab Zaqout, Roziati Zainuddin, Sapian Baba,.: Pixel-Based Skin Color Detection Technique, in Machine Graphics and Vision, 2005. FLEXChip Signal Processor (MC68175/D), Motorola, 1996. 6. Qiu-yu Zhang, Mo-yi Zhang, Jian-qiang Hu,.: Hand Gesture Contour Tracking Based on Skin Color Probability and State Estimation Model, Journal of Multimedia, Vol. 4, No. 6, December 2009. A. Karnik, “Performance of TCP congestion control with rate feedback: TCP/ABR and rate adaptive TCP/IP,” M. Eng. thesis, Indian Institute of Science, Bangalore, India, Jan. 1999. 7. Lars Bretzner, Ivan Laptev, Tony Lindberg,.: Hand Gesture Recognition using Multi-Scale Colour Features, Hierarchical Models and Particle Filtering , Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition (2002). Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification, IEEE Std. 802.11, 1997. References
  29. 29. Contd… • G. Lee and N. Y. Chong, “Decentralized formation control for small scale robot teams with anonymity,” Mechatronics, vol. 19, no. 1, pp.85– 105,2009. • G. Lee and N. Y. Chong, “A geometric approach to deploying robot swarms,” Ann. Math. Artif. Intell., vol. 52, no. 2–4, pp. 257–280, 2009. • E. Sahin, “Swarm robotics: From sources of inspiration to domains of • application,” in Proc. 8th Int. Conf. Simulation of Adaptive Behavior(LNCS),2005,vol.3342,pp.10–20. • H. Niwa, K. Kodaka, Y. Sakamoto, M. Otake, S. Kawaguchi, K. Fujii, Y. Kanemori, and S. Sugano, “GPS-based indoor positioning system with multi-channel pseudolite,” in Proc. IEEE Int. Conf. Robot. Autom., 2008, pp. 905–910. Faraj Alhwarin, Chao Wang, Danijela Risti -Durrant, Axel Graser, Improved SIFT-Features Matching for Object Recognition, BCS International Academic Conference- Visions of Computer Science,2008.
  30. 30. Thank you