Automated Audience Polling On Iphone


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Automated Audience Polling On Iphone

  1. 1. Automated Audience Polling On IPhone BHAVIK PANCHAL WIMC 28-APR-2014
  2. 2. Outline • Introduction • Algorithm and framework • System Configuration • Preprocessing • Finding Raised Hands • Implementation • Conclusions
  3. 3. Introduction 1) A system for the automatic visual estimation of in-class polling using an iPhone is proposed which detects the number of faces within the field of view, 2) The number of raised hands within the field of view, and uses the ratio as the estimate of the number of positive votes. 3) The system utilizes the Adaboost object detection algorithm implemented in OpenCV, and processes all detected faces and raised hands, to determine whether each of the audience available in the scene are voting or not. 4) The system works as a standalone iPhone application. So, the whole process of audience polling is handled by a single iPhone.
  4. 4. Algorithms And Framework 1) Adaboost Algorithm : It use for face detection and compute the face location. 2) OpenCV(Open Source Compute Vision) : A library of programming functions mainly aimed at real-time computer use open framework. 3) ROI(Region Of Interest) : A selected subset of samples within a dataset identified for a particular purpose.
  5. 5. System Configuration Fig. 1.Sample images of an audience (photos are taken with iPhone).
  6. 6. 1) The regions of the image that pass all layers of this cascade classifier are then considered as detected objects. Fig .2.Sample images of an audience (photos are taken with iPhone).
  7. 7. Preprocessing 1) Before starting the main procedure for finding raised hands in the image, the image is processed to find the faces. 2) Its Detect the face through Adaboost Algorithm. Fig. 3. Faces detected using Adaboost algorithm(N==8)
  8. 8. 3) Region of interest (ROI) for finding hands based on the detected faces. Fig. 4. Faces with Hand detected using OpenCV.
  9. 9. Finding Raised Hands 1) OpenCV has the ability to train a new hand detector if it is provided with a large number (1,000–10,000) of positive (hand) and negative images. Fig.5. Aligning all hand images with a reference image through a rigid-body spatial registration.
  10. 10. Implementation 1) Used OpenCV (Cross Platform ):Using Objective-C Language 2) Facial recognition system. 3) Gesture recognition. 4) Image processing.
  11. 11. 1) implemented a completely automatic system for polling an audience using iPhone. The whole process of audience polling is handled by a single iPhone. 2) The system then finds the people and their hands in the image and reports the polling results. 3) The complete audience polling system has been successfully implemented on iPhone where the whole process of polling the audience takes Formula seconds to complete CONCLUSION
  12. 12. References 1) “Automated audience polling on iPhone" Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on, Issue Date: 5-8 May 2013, Written by: Heidari, A.; Aarabi, P. 2) Paul Viola and Michael J. Jones, “Robust real-time face detection” International Journal of Computer Vision, 2004. 3) H. Nolker, C.; Ritter, “Visual recognition of continuous hand postures” Neural Networks, IEEE Transactions on,2002.