Hand Detection with a Cascade of Boosted Classifiers Using Haar-like Features Qing Chen Discover Lab, SITE, University of Ottawa May 2, 2006.

Slides:



Advertisements
Similar presentations
EE462 MLCV Lecture 5-6 Object Detection – Boosting Tae-Kyun Kim.
Advertisements

Rapid Object Detection using a Boosted Cascade of Simple Features Paul Viola, Michael Jones Conference on Computer Vision and Pattern Recognition 2001.
Rapid Object Detection using a Boosted Cascade of Simple Features Paul Viola, Michael Jones Conference on Computer Vision and Pattern Recognition 2001.
Face detection Behold a state-of-the-art face detector! (Courtesy Boris Babenko)Boris Babenko.
Face Detection & Synthesis using 3D Models & OpenCV Learning Bit by Bit Don Miller ITP, Spring 2010.
Presenter: Hoang, Van Dung
AdaBoost & Its Applications
Face detection Many slides adapted from P. Viola.
Cos 429: Face Detection (Part 2) Viola-Jones and AdaBoost Guest Instructor: Andras Ferencz (Your Regular Instructor: Fei-Fei Li) Thanks to Fei-Fei Li,
EE462 MLCV Lecture 5-6 Object Detection – Boosting Tae-Kyun Kim.
The Viola/Jones Face Detector Prepared with figures taken from “Robust real-time object detection” CRL 2001/01, February 2001.
The Viola/Jones Face Detector (2001)
HCI Final Project Robust Real Time Face Detection Paul Viola, Michael Jones, Robust Real-Time Face Detetion, International Journal of Computer Vision,
Rapid Object Detection using a Boosted Cascade of Simple Features
Robust Real-time Object Detection by Paul Viola and Michael Jones ICCV 2001 Workshop on Statistical and Computation Theories of Vision Presentation by.
Face detection and recognition Many slides adapted from K. Grauman and D. Lowe.
A Robust Real Time Face Detection. Outline  AdaBoost – Learning Algorithm  Face Detection in real life  Using AdaBoost for Face Detection  Improvements.
Adaboost and its application
CS 223B Assignment 1 Help Session Dan Maynes-Aminzade.
A Robust Real Time Face Detection. Outline  AdaBoost – Learning Algorithm  Face Detection in real life  Using AdaBoost for Face Detection  Improvements.
Robust Real-Time Object Detection Paul Viola & Michael Jones.
Viola and Jones Object Detector Ruxandra Paun EE/CS/CNS Presentation
Dynamic Cascades for Face Detection 第三組 馮堃齊、莊以暘. 2009/01/072 Outline Introduction Dynamic Cascade Boosting with a Bayesian Stump Experiments Conclusion.
Viewpoint Tracking for 3D Display Systems A look at the system proposed by Yusuf Bediz, Gözde Bozdağı Akar.
Foundations of Computer Vision Rapid object / face detection using a Boosted Cascade of Simple features Presented by Christos Stoilas Rapid object / face.
F ACE D ETECTION FOR A CCESS C ONTROL By Dmitri De Klerk Supervisor: James Connan.
Face Detection CSE 576. Face detection State-of-the-art face detection demo (Courtesy Boris Babenko)Boris Babenko.
FACE DETECTION AND RECOGNITION By: Paranjith Singh Lohiya Ravi Babu Lavu.
AdaBoost Robert E. Schapire (Princeton University) Yoav Freund (University of California at San Diego) Presented by Zhi-Hua Zhou (Nanjing University)
Face Detection using the Viola-Jones Method
Human tracking and counting using the KINECT range sensor based on Adaboost and Kalman Filter ISVC 2013.
A Tutorial on Object Detection Using OpenCV
Using Statistic-based Boosting Cascade Weilong Yang, Wei Song, Zhigang Qiao, Michael Fang 1.
EADS DS / SDC LTIS Page 1 7 th CNES/DLR Workshop on Information Extraction and Scene Understanding for Meter Resolution Image – 29/03/07 - Oberpfaffenhofen.
PortableVision-based HCI A Hand Mouse System on Portable Devices 連矩鋒 (Burt C.F. Lien) Computer Science and Information Engineering Department National.
Object Detection Using the Statistics of Parts Presented by Nicholas Chan – Advanced Perception Robust Real-time Object Detection Henry Schneiderman.
Detecting Pedestrians Using Patterns of Motion and Appearance Paul Viola Microsoft Research Irfan Ullah Dept. of Info. and Comm. Engr. Myongji University.
Window-based models for generic object detection Mei-Chen Yeh 04/24/2012.
Sign Classification Boosted Cascade of Classifiers using University of Southern California Thang Dinh Eunyoung Kim
Lecture 29: Face Detection Revisited CS4670 / 5670: Computer Vision Noah Snavely.
Face detection Slides adapted Grauman & Liebe’s tutorial
DIEGO AGUIRRE COMPUTER VISION INTRODUCTION 1. QUESTION What is Computer Vision? 2.
Terrorists Team members: Ágnes Bartha György Kovács Imre Hajagos Wojciech Zyla.
Robust Real-time Face Detection by Paul Viola and Michael Jones, 2002 Presentation by Kostantina Palla & Alfredo Kalaitzis School of Informatics University.
ECE738 Advanced Image Processing Face Detection IEEE Trans. PAMI, July 1997.
Automated Solar Cavity Detection
Adaboost and Object Detection Xu and Arun. Principle of Adaboost Three cobblers with their wits combined equal Zhuge Liang the master mind. Failure is.
HCI/ComS 575X: Computational Perception Instructor: Alexander Stoytchev
Lecture 09 03/01/2012 Shai Avidan הבהרה: החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת.
The Viola/Jones Face Detector A “paradigmatic” method for real-time object detection Training is slow, but detection is very fast Key ideas Integral images.
Bibek Jang Karki. Outline Integral Image Representation of image in summation format AdaBoost Ranking of features Combining best features to form strong.
Learning to Detect Faces A Large-Scale Application of Machine Learning (This material is not in the text: for further information see the paper by P.
Hand Gesture Recognition Using Haar-Like Features and a Stochastic Context-Free Grammar IEEE 高裕凱 陳思安.
FACE DETECTION : AMIT BHAMARE. WHAT IS FACE DETECTION ? Face detection is computer based technology which detect the face in digital image. Trivial task.
Lecture 10 Pattern Recognition and Classification II
Face detection Behold a state-of-the-art face detector! (Courtesy Boris Babenko)Boris Babenko slides adapted from Svetlana Lazebnik.
A Brief Introduction on Face Detection Mei-Chen Yeh 04/06/2010 P. Viola and M. J. Jones, Robust Real-Time Face Detection, IJCV 2004.
Face detection Many slides adapted from P. Viola.
Face Detection and Recognition Reading: Chapter and, optionally, “Face Recognition using Eigenfaces” by M. Turk and A. Pentland.
AdaBoost Algorithm and its Application on Object Detection Fayin Li.
1 Munther Abualkibash University of Bridgeport, CT.
Reading: R. Schapire, A brief introduction to boosting
2. Skin - color filtering.
License Plate Detection
Session 7: Face Detection (cont.)
Learning to Detect Faces Rapidly and Robustly
ADABOOST(Adaptative Boosting)
A Tutorial on Object Detection Using OpenCV
Lecture 29: Face Detection Revisited
ECE738 Final Project Face Detection Baseline
Presentation transcript:

Hand Detection with a Cascade of Boosted Classifiers Using Haar-like Features Qing Chen Discover Lab, SITE, University of Ottawa May 2, 2006

2 Outline  1. Introduction  2. Haar-like features  3. Adaboost  4. The Cascade of Classifiers  5. Preliminary Results  6. Future Work

3 1. Introduction  Hand-based Human Computer Interface (HCI) should meet the requirements of real-time, accuracy and robustness.  The purpose of Haar-like features is to meet the real-time requirement.  The purpose of the cascade of Adaboosted (Adaptive boost) classifiers is to achieve both accuracy and speed.  The algorithm has been used for face detection which achieved high detection accuracy and approximately 15 times faster than any previous approaches.  The algorithm is a generic objects detection/recognition method.

4 2. Haar-Like Features  Each Haar-like feature consists of two or three jointed “ black ” and “ white ” rectangles:  The value of a Haar-like feature is the difference between the sum of the pixel gray level values within the black and white rectangular regions: f(x)=Sum black rectangle (pixel gray level) – Sum white rectangle (pixel gray level)  Compared with raw pixel values, Haar-like features can reduce/increase the in-class/out-of-class variability, and thus making classification easier. Figure 1: A set of basic Haar-like features. Figure 2: A set of extended Haar-like features.

5 2. Haar-Like Features (cont ’ d)  The rectangle Haar-like features can be computed rapidly using “ integral image ”.  Integral image at location of x, y contains the sum of the pixel values above and left of x, y, inclusive:  The sum of pixel values within “ D ” : AB C D P2P2 P3P3 P4P4 P1P1 P (x, y)

6 2. Haar-Like Features (cont ’ d)  To detect the hand, the image is scanned by a sub-window containing a Haar-like feature.  Based on each Haar-like feature f j, a weak classifier h j (x) is defined as: where x is a sub-window, and θ is a threshold. p j indicating the direction of the inequality sign.

7 3. Adaboost  The computation cost using Haar-like features: Example: original image size: 320X240, sub-window size: 24X24, frame rate: 15 frame/second, The total number of sub-windows with one Haar-like feature per second: ( )X( )X15=966,735 Considering the scaling factor and the total number of Haar-like features, the computation cost is huge.  AdaBoost (Adaptive Boost) is an iterative learning algorithm to construct a “ strong ” classifier using only a training set and a “ weak ” learning algorithm. A “ weak ” classifier with the minimum classification error is selected by the learning algorithm at each iteration.  AdaBoost is adaptive in the sense that later classifiers are tuned up in favor of those sub-windows misclassified by previous classifiers.

8 3. Adaboost (cont ’ d)  The algorithm:

9  Adaboost starts with a uniform distribution of “ weights ” over training examples. The weights tell the learning algorithm the importance of the example.  Obtain a weak classifier from the weak learning algorithm, h j (x).  Increase the weights on the training examples that were misclassified.  (Repeat)  At the end, carefully make a linear combination of the weak classifiers obtained at all iterations. 3. Adaboost (cont ’ d)

10 4. The Cascade of Classifiers  A series of classifiers are applied to every sub-window.  The first classifier eliminates a large number of negative sub-windows and pass almost all positive sub-windows (high false positive rate) with very little processing.  Subsequent layers eliminate additional negatives sub-windows (passed by the first classifier) but require more computation.  After several stages of processing the number of negative sub-windows have been reduced radically.

11 4. The Cascade of Classifiers (cont ’ d)  Negative samples: non-object images. Negative samples are taken from arbitrary images. These images must not contain object representations.  Positive samples: images contain object (hand in our case). The hand in the positive samples must be marked out for classifier training.

12 5. Preliminary Results  Number of pos. samples: 144  Number of neg. samples: 3142  Sample Resolution: 640X480  Initial sub-window size: 15X30  Scale factor: 1.3  Cascade obtained: 12 grades

13 6. Future Work  Extended Haar-like features? Will extended Haar-like features improve the detection accuracy? (Still an Open Problem) The performance tradeoff?  Parallel cascades for multiple hand gestures. How to select the hand gesture configurations which can be detected more effectively with the employed Haar-like feature set?  Improve the robustness against hand rotation.  How much improvement can be achieved with more training samples? Intel face detection classifier: 5000 Pos Neg. Accuracy: 98%

14 References:  Wu Bo, et al., “ A Multi-View Face Detection Based on Real Adaboost Algorithm, ” Computer Research and Development, 42 (9):pp ,2005.  Paul Viola and Michael J. Jones, “ Robust Real-time Object Detection, ” Technical Report, Cambridge Research Lab, Compaq  Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies, “ Analysis of Boosting Algorithms using the Smooth Margin Function: A Study of Three Algorithms, ”  Rainer Lienhart, Alexander Kuranov, Vadim Pisarevsky, “ Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection, ” MRL Technical Report, May  Andre L. C. Barczak, Farhad Dadgostar, “ Real-time Hand Tracking Using a Set of Cooperative Classifiers and Haar-Like Features, ” Research Letters in the Information and Mathematical Sciences, ISSN , Vol. 7, pp 29-42,  Mathias K ö lsch and Matthew Turk, “ Robust Hand Detection, ” Proc. IEEE Intl. Conference on Automatic Face and Gesture Recognition, May  Intel OpenCV Documents.  Acknowledgement goes to Urtho ’ s training data for eye detection and F. Dadgostar ’ s hand palm database.

15 Thank you and Any Questions?