Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick CS 7616 Pattern Recognition Introduction Aaron Bobick School of Interactive Computing
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Where are you? • CS 7616 Pattern Recognition • Web site: http://www.cc.gatech.edu/~afb/classes/CS7616-Spring2014/ • Will have posted calendar/syllabus with posted slides, problem sets with data, other administrative stuff. • T-square: The usual stuff. There is/will be a web page under resources that points to the class web page listed above. • Slides: PDFs will be posted by linking to the calendar. Hopefully draft by class time, but I wouldn’t count on it. • Piazza: You will all receive an invitation to Piazza for CS7616. If not, send us email. This was invaluable for discussions for problem sets in CS4495. Not sure if here. • Announcements will likely be done through both T-Square (and email) and Piazza • Matlab access : if you don’t know how to get Matlab access, first ask a friend. Then come see TAs or me. If you want to use Python/Numpy that’s OK.
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Who are you? • From the web site: This is a graduate level for those interested in pattern recognition in general and for some elements as applied to computer vision. It is *not* going to be a comprehensive Machine Learning course. • What do you know? • A good foundation of probability and linear algebra. This class will have more math in it than most Computer Science classes. • Any Machine Learning background will help. Though the course won’t technically presume ML as a background it will be much easier to grasp if you’ve seen things like graphical models or other inference structures. • A good working knowledge of Matlab or Python with Numpy. We will likely be doing things in Matlab in class. I am pretty sure that Octave will be OK though the lack of some plotting make may some figures harder to generate. Because this is my first time offering this class I can only speculate on this.
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Who are we? Professor: TA: Aaron Bobick Abhijit Kundu afb@cc.gatech.edu abhijit.dgp@gmail.com Office: CCB 316 Office: CCB 308 Office hours: Office hours: TBD Tues 1-2pm (email is much better)
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick What will you read? Hastie, Tibshirani, and • Kevin Murphy’s book: Friedman: Machine Learning: a The Elements of Probabilistic Statistical Learning Perspective (free pdf)
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick What you will do? • NB: This is the first time I have taught this class. This is a plan, maybe not even a plan but a goal, certainly not a commitment ! • Your grade is mostly project based set based. • Communal projects (everyone does mostly the same): 60% • Your unique final project 30% • Class presentation (10% - but class size may revamp this) • Class participation – max of 10% (ie it can only help your grade). • There will not be a final exam.
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick When will you do them? • You will have plenty of time for the projects. But they must be submitted on time. • Late submissions will only be accepted at full credit with prior approval. Otherwise 50% (yes half) reduction. • TA/Prof *not* obligated to get back to you about permission the weekend it is due! At your own risk. • I will be very adamant about this. Not fair to others or the TA.
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick With whom will you do it? • Honesty/Integrity policy (from web site): Problem sets are to be done individually (or within your group of no more than two) but you may collaborate at the “white board level” helping each other with algorithms and general computation, BUT YOUR CODE MUST BE YOUR OWN. • Do not hand in other people’s code unless you (1) say you are, and (2) you want no credit for that section. We will be explicit about what previous or provided code you can use.
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick How will you do it? • We will mostly support Matlab • I know how to program Matlab. • I am not going to use R. • I am not going to (deeply) learn Python. • All the algorithms and demos in Murphy’s book are available in Matlab. • If you want to use some thing else • Fine • Your job to make it work. • TA not obligated to help.
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Any questions so far…
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Who am I… • Background: degrees in Math, CS but PhD in CogSci/AI; interest in high level perception and cognition • Faculty at the MIT Media Lab for a while – developed lots of work in machine understanding of action from video • Came here 14 years ago, and collected hats: • Was GVU director • Founding chair of the School of Interactive Computing • But the most fun is being professor in Computational Perception and Robotics!
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Research I used to do… Action recognition from video • Lots of domains/levels of complexity: • Body motions • Gesture recognition, • Football plays • Aware Home • Surveillance
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick What I am doing now… Robots that “see”… not (just) a question of geometry but understanding. Indoor Outdoor Human Robot Interaction
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Two specific projects: (Vision for) Human-robot collaboration in manufacturing (with BMW!) Affordance-based perception: Robot learning the “affordances” of objects and how to use in planning and acting.
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Why am I teaching this class? • Because I have to teach another class (besides Computer Vision) … • Because I used to teach PR from Duda and Hart (and Stork) which was written somewhere between the Stone Age and the invention of the Prius… • Because I keep running into PR problems and I really should understand them better…
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick A few inspirations…
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Boosting Simple Features [Viola and Jones CVPR 01] • Adaboost classification Strong Weak 𝑈 classifier classifier 𝑔 𝑦 = � 𝛽 𝑢 ℎ 𝑢 𝑦 𝑢=1 • Weak classifiers: Haar-basis like functions (45,396 in total) ℎ 𝑢 𝑦 = � 1 if 𝑔 𝑢 ( 𝑦 ) > 0 otherwise − 1 18
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Boosting Simple Features [Viola and Jones CVPR 01] • Integral image • A value at (x,y) is the sum of the pixel values above and to the left of (x,y). • The sum of original image values within the rectangle can be computed: Sum = A-B-C+D 19 ICCV09 Tutorial Tae-
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Body tracking in Microsoft Kinect for XBox 360 left hand right foot right neck shoulder Input depth image Training labelled data Visual features Classification forest Labels are categorical Objective function Input data point Node parameters Visual features Node training Feature response Weak learner Predictor model
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Body tracking in Microsoft Kinect for XBox 360 Input depth image (bg removed) Inferred body parts posterior (2 videos here)
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick But what did I really want to do?
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick A “simple” detection problem Is it a real person?
Introduction Introduction CS7616 Pattern Recognition – A. Bobick CS7616 Pattern Recognition – A. Bobick Anyone in the tunnel?
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