WEBCAS (WEBCAM BASED CLASSROOM ATTENDANCE SYSTEM)

Slides:



Advertisements
Similar presentations
1/12 Vision based rock, paper, scissors game Patrik Malm Standa Mikeš József Németh István Vincze.
Advertisements

Computational Biology, Part 23 Biological Imaging II Robert F. Murphy Copyright  1996, 1999, All rights reserved.
Stanford hci group / cs376 research topics in human-computer interaction Vision-based Interaction Scott Klemmer 17 November 2005.
LYU0603 A Generic Real-Time Facial Expression Modelling System Supervisor: Prof. Michael R. Lyu Group Member: Cheung Ka Shun ( ) Wong Chi Kin ( )
LYU0603 A Generic Real-Time Facial Expression Modelling System Supervisor: Prof. Michael R. Lyu Group Member: Cheung Ka Shun ( ) Wong Chi Kin ( )
OpenCV Stacy O’Malley CS-590 Summer, What is OpenCV? Open source library of functions relating to computer vision. Cross-platform (Linux, OS X,
Tracking Migratory Birds Around Large Structures Presented by: Arik Brooks and Nicholas Patrick Advisors: Dr. Huggins, Dr. Schertz, and Dr. Stewart Senior.
Face Recognition and Biometric Filters By Fred_the_token Identity Confirmed: Osama bin Laden.
Real-time Hand Pose Recognition Using Low- Resolution Depth Images
Original image: 512 pixels by 512 pixels. Probe is the size of 1 pixel. Picture is sampled at every pixel ( samples taken)
Eye Tracking Project Project Supervisor: Ido Cohen By: Gilad Ambar
1 Chapter 21 Machine Vision. 2 Chapter 21 Contents (1) l Human Vision l Image Processing l Edge Detection l Convolution and the Canny Edge Detector l.
Behavior Analysis Midterm Report Lipov Irina Ravid Dan Kotek Tommer.
Real-Time Face Detection and Tracking Using Multiple Cameras RIT Computer Engineering Senior Design Project John RuppertJustin HnatowJared Holsopple This.
Ahmed Abdel-Fattah Jerry Chang Derrick Culver Matt Zenthoefer.
Remote Surveillance System Presented by: Robarin Holdings Limited Telephone: Facsimile:
Matthias Wimmer, Bernd Radig, Michael Beetz Chair for Image Understanding Computer Science Technische Universität München Adaptive.
Vision-Based Biometric Authentication System by Padraic o hIarnain Final Year Project Presentation.
Conference Room Laser Pointer System Preliminary Design Report Anna Goncharova Brent Hoover Alex Mendes.
ICBV Course Final Project Arik Krol Aviad Pinkovezky.
2D TO 3D MODELLING KCCOE PROJECT PRESENTATION Student: Ashish Nikam Ashish Singh Samir Gaykar Sanoj Singh Guidence: Prof. Ashwini Jaywant Submitted by.
IT Introduction to Information Technology CHAPTER 05 - INPUT.
Knowledge Systems Lab JN 8/24/2015 A Method for Temporal Hand Gesture Recognition Joshua R. New Knowledge Systems Laboratory Jacksonville State University.
Shape Recognition and Pose Estimation for Mobile Augmented Reality Author : N. Hagbi, J. El-Sana, O. Bergig, and M. Billinghurst Date : Speaker.
Knowledge Systems Lab JN 9/10/2002 Computer Vision: Gesture Recognition from Images Joshua R. New Knowledge Systems Laboratory Jacksonville State University.
Image Collection Backend for Cameraphones. Introduction Project Goals Design an integrated system to upload image from a mobile phone to a remote server.
Submitted by:- Vinay kr. Gupta Computer Sci. & Engg. 4 th year.
Portable Vision-Based HCI A Real-Time Hand Mouse System on Portable Devices 連矩鋒 (Burt C.F. Lien) Department of Computer Science and Information Engineering.
Application in Computer Vision Final Project Nir Slakman, Oren Zur and Noam Ben-Ari.
Chapter 8 Problems Prof. Sin-Min Lee Department of Mathematics and Computer Science.
TELEKINESYS Group Members: Mir Murtaza SM Rasikh Mukarram Shiraz Sohail.
DEVELOPMENT OF ALGORITHM FOR PANORAMA GENERATION, AND IMAGE SEGMENTATION FROM STILLS OF UNDERVEHICLE INSPECTION Balaji Ramadoss December,06,2002.
Real-Time Cyber Physical Systems Application on MobilityFirst Winlab Summer Internship 2015 Karthikeyan Ganesan, Wuyang Zhang, Zihong Zheng.
September 23, 2014Computer Vision Lecture 5: Binary Image Processing 1 Binary Images Binary images are grayscale images with only two possible levels of.
1 COMS 161 Introduction to Computing Title: Digital Images Date: November 12, 2004 Lecture Number: 32.
infinity-project.org Engineering education for today’s classroom 2 Outline How Can We Use Digital Images? A Digital Image is a Matrix Manipulating Images.
REAL TIME FACE DETECTION
Submitted by : Mark Gakman, Herzel Abramov Supervisors : Ina Rivkin, Eli Shoushan Vitaly Savuskan, Avi Hohama, Prof. Yael Nemirovsky.
Scanning Basics. An image can be created, opened, edited, and saved in over a dozen different file formats in Photoshop. Of these, you might use only.
Implementation of a Relational Database as an Aid to Automatic Target Recognition Christopher C. Frost Computer Science Mentor: Steven Vanstone.
CSCI-100 Introduction to Computing Hardware Part II.
CSE 140: Computer Vision Camillo J. Taylor Assistant Professor CIS Dept, UPenn.
Team Members Ming-Chun Chang Lungisa Matshoba Steven Preston Supervisors Dr James Gain Dr Patrick Marais.
Su-ting, Chuang 1. Outline Introduction Related work Hardware configuration Detection system Optimal parameter estimation framework Conclusion 2.
Spring 2007 COMP TUI 1 Computer Vision for Tangible User Interfaces.
Motion Detection and Processing Performance Analysis Thomas Eggers, Mark Rosenberg Department of Electrical and Systems Engineering Abstract Histograms.
Video Surveillance Under The Guidance of Smt. D.Neelima M.Tech., Asst. Professor Submitted by G. Subrahmanyam Roll No: 10021F0013 M.C.A.
CONTENT FOCUS FOCUS INTRODUCTION INTRODUCTION COMPONENTS COMPONENTS TYPES OF GESTURES TYPES OF GESTURES ADVANTAGES ADVANTAGES CHALLENGES CHALLENGES REFERENCE.
Augmented Reality and 3D modelling Done by Stafford Joemat Supervised by Mr James Connan.
Real-Time Dynamic Shadow Algorithms Evan Closson CSE 528.
Su-ting, Chuang 1. Outline Introduction Related work Hardware configuration Finger Detection system Optimal parameter estimation framework Conclusion.
Graphics II Image Processing I. Acknowledgement Most of this lecture note has been taken from the lecture note on Multimedia Technology course of University.
SUREILLANCE IN THE DEPARTMENT THROUGH IMAGE PROCESSING F.Y.P. PRESENTATION BY AHMAD IJAZ & UFUK INCE SUPERVISOR: ASSOC. PROF. ERHAN INCE.
2008 Student Progress Monitoring & Data-Based Instruction in Special Education Introduction to Using CBM for Progress Monitoring.
Copyright © Cengage Learning. All rights reserved. Normal Curves and Sampling Distributions 7.
Hand Detection with a Cascade of Boosted Classifiers Using Haar-like Features Qing Chen Discover Lab, SITE, University of Ottawa May 2, 2006.
Software Design and Development Storing Data Part 2 Text, sound and video Computing Science.
Che-An Wu Background substitution. Background Substitution AlphaMa p Trimap Depth Map Extract the foreground object and put into another background Objective.
CONTENTS:  Introduction.  Face recognition task.  Image preprocessing.  Template Extraction and Normalization.  Template Correlation with image database.
Over the recent years, computer vision has started to play a significant role in the Human Computer Interaction (HCI). With efficient object tracking.
Hand Gestures Based Applications
Face Detection and Notification System Conclusion and Reference
Group 4 Alix Krahn Denis Lachance Adam Thomsen
Group 4 Alix Krahn Denis Lachance Adam Thomsen
IMAGE SEGMENTATION USING THRESHOLDING
Object Recognition in Video Games
Elecbits Electronic shade.
The Image The pixels in the image The mask The resulting image 255 X
Midway Design Review Team 1: MirrAR
Presentation transcript:

WEBCAS (WEBCAM BASED CLASSROOM ATTENDANCE SYSTEM) ADITYA KAWATRA ARPIT MAHESHWARI MANISH MEHTA ANURAG KUMAR

OBJECTIVES OF THE PROJECT To take attendance of a class with known strength and dimensions using webcams and to develop a hardware software interface for the same

DESIGN METHODOLGY The project involved taking pictures of a classroom using webcams and analyzing them. The image processing software used for this purpose was OpenCV.

ABOUT OPEN CV (OPEN Source Computer Vision) is a library of real-time computer vision routines from Intel. It was first released in 2000, and is used in applications such as object, face and gesture recognition, lip reading and motion tracking.

METHOD USED… We decided to divide the classroom into two distinct parts, with one webcam being allotted for each part.

DIAGRAM 1

METHOD (CONTD) To take attendance the photographs taken were first converted to grayscale. Masks were then generated of each student in the classroom. On multiplying the mask with the original grayscale image, an individual student’s photograph was recovered.

METHOD (CONTD) We utilized the fact that humans almost always move in an interval of time. As a result, two photographs taken within a small interval, on subtraction would yield the outline of a human face. Thus, two photographs are taken from each camera, 2 seconds apart in time. Both these images are converted to grayscale and multiplied with masks. Corresponding results are then subtracted.

METHOD (CONTD) A suitable threshold was assigned to the value of the sum of pixels in the subtracted image (taken as 0.0001 based on empirical evidence) Exceeding the threshold means the presence of a human face in the photograph. Finally, the no. of faces is counted and stored.

FLOWCHART

REQUIREMENTS AND ASSUMPTIONS Ideally, a photograph of the entire class for purposes of reference (for creation of masks). Suitable illumination. It is assumed that the students always sit at the same place in their classes.

SAMPLES The following slides show a sample of our implementation. These contain the original images, the converted grayscale photographs, the mask, the results of multiplication and the difference operations.

HARDWARE USED Logitech Quick Cam Easy webcams (2) with still resolution of 1.3 MPixel, video of 320x240 at 30fps. Wooden stands made for purpose of fixing the cameras in their desired positions.

STRENGTHS OF THE SYSTEM Extremely simple and user-friendly Does everything with a few clicks Creates attendance logs for future reference Can also be used for live CCTV feed if needed

SUGGESTIONS AND LIMITATIONS More rigid supports for the cameras would be extremely beneficial OpenCV does not recognize two cameras at a time

COSTS UNDERTAKEN 2 webcams : Rs 3400/- 2 clamps : Rs 250/- 2 connecting cables : Rs 50/- Miscellaneous : Rs 250/-

ACKNOWLEDGEMENTS We would like to thank Prof Ajay Kumar Pathak for his constant (though strict) support and guidance throughout the project.