TeachingWs19TDCV

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

TeachingWs19TDCV

Tracking and Detection in Computer Vision - Winter Term 2019/20

Administrative Info

Lecture by Dr. Slobodan Ilic
Exercises by: Mai Bui, Mahdi Hamad,Ivan Shugurov, Agnieszka Tomczak

Type: Lecture Module IN2210
Programs: Informatics (Bachelor, Master)
Biomedical Computing (Master)
Robotics, Cognition, Intelligence(Master)
SWS: 2+4
ECTS: 7 Credits
Course Language: English

Time, Location & Requirements

Lectures: Mondays, from 21.10.19 at 14:00-16:00h at 00.13.009A.

Exercises: Thursdays, from 24.10.19 at 14:00-16:00h at 00.13.009A (max 70 participants), 03.09.012(20 places) and 03.11.018(20 places)

Requirements:

  • The classes and exams are in English.
  • For the midterm exam, nothing but your pens and your calculators are allowed.
  • For the final exam, nothing but your pens, your calculators and one DIN A4 page (handwritten, front and back side) with notes are allowed.
  • There is no repetition exam.

Contents

Overview

Computer Vision has emerged as a key discipline in computer science. Stemming from Artificial Intelligence(AI) with the ultimate goal to understand Human Vision as one of the most powerful human senses it made a long way to what it is nowadays. We are witnessing a big come back of Computer Vision to its roots "Artificial Intelligence" in the recent years. Recent boost in Machine Learning and Deep Learning powered by incredible computational power of modern GPUs and profiting from massive amount of digital data clearly changed the way how CV research is done at the moment and what impact it has on the society. This is not only evident by a growing and highly competitive research community with a high impact factor in computer science, but also by the emergence of numerous vision companies turning research ideas into a myriad of commercial applications. Almost all big technology companies are at on the edge with Computer Vision, AI and machine learning nowadays.

Besides well-known studies of 3D geometry and camera models, object tracking and detection in images and videos becomes one of the principal research directions of modern Computer Vision. The main objective of this course is to provide students with a gradual introduction to object detection and recognition techniques which proved to be widely used in the past and in current Computer Vision research. The course will provide in-depth knowledge of image features, their detection and description, matching techniques, key-point recognition, basic and advanced tracking algorithms based on image features and image intensities. The course will address both hand crafted features and learned features with deep learning approaches. So it will be mixture of established and emerging methods for object detection, recognition and pose estimation.

In the end, the students will have a thorough description of the most important object detection and pose estimation techniques. They should be able to understand and implement those solutions and apply them in reasonably complex problems. The concepts described in this course will be accompanied with brief explanations of the necessary mathematical tools. The participants of this course will be given an important basis to follow the vast and growing Computer Vision literature and use the acquired knowledge to solve new practical problems.

Announcements

  • First lecture will take place on 21.10. at 00.13.009A. Due to limited number of places it is recommended to come earlier to get a seat. After several lectures usually there is enough available seats.
  • First tutorial session will take place on 24.10 at 00.13.009A (max 70 participants), and if necessary also in 03.09.012(20 places) and 03.11.018(20 places). This will be decided on the place, but all students should come to 00.13.009A initially.
  • Mid-term exam will take place on 05.12. from 18:30 bis 20:15h at Hörsaal MI-HS-1
  • An additional Q&A session for Homework 1.2 will take place at 18:30h on Monday 09.12.19 at 00.13.009A

Exercise projects

The project will be done in MATLAB, C++ and Python and students are expected to do them on their own computer/laptop. They will consist of three projects that will include one or several topics presented at the lectures. The projects will be presented to the students during the exercise sessions, where they will be instructed how to do the projects. Each project is divided into a number of exercises and each of them corresponds to a homework which will be graded. In total all homework exercises will result in one exercise grade. The deadlines for the submissions of the results of the homeworks will be announced via Moodle.

Students have to make groups of up to FOUR students. If you are looking for a (TDCV) partner tell us and we will assign you one!

During the the presentation of the results for each project each group will have 10 min to present their solution, and also run the code live. This will be followed by questions from the tutors (~ 10min). All members of the group will be questioned about the algorithms and their implementation, so only those who can prove that they understood both theory and practice will get the respective points. Thus, having a working exercise doesn't mean that everyone in the group is going to get full points. Please note that only exercises which work might get the maximal number of points. There will be no "debugging-correcting"-sessions for your solutions during the project results presentation! If you have questions, ask them beforehand during the exercise sessions.

Registration

You are required to register via TUMOnline for the course. In case you are on the waiting list you will automatically be registered as soon as some of the registered students deregister. We kindly ask registered students who do not intend to attend the course and take the exam to deregister and make space for those on the waiting list.

Who should attend this course?

Bachelor students in their last years and all Master students that are interested in learning about the modern and well-established concepts and algorithms related to object detection, recognition and pose estimation in Computer Vision, or desire to use those techniques in their research.

Prerequisites: Most of the knowledge required should be part of the normal background in Computer Science, undergraduate/graduate Mathematics and Geometry. However, strong knowledge in linear algebra and calculus will make much easier to follow the course. Moreover, due to large part of the course is dedicated to project homeworks and implementations in MATLAB, C++ and python it is necessary to have good knowledge of at least two of the programming languages.

Textbooks

For the geometry part of the course we recommend "Multiple-View Geometry" book of A. Ziserman and R. Hartely or respected papers attached to the slides, which will appear on this webpage. For machine learning topics it is recommended to use the book of Christopher Bishop, Pattern Recognition and Machine Learning. And for deep learning, the Stanford course on CNNs available on-line. . However, since the majority of the topics is state-of-the-art, students will be referred to the relevant publications which will be provided during the course and can be regarded as additional lecture notes.

Lecture Schedule and Content

Date Topic Conducted by Material/Literature
14.10. No Lecture Dr. S. Ilic  
21.10. Introduction Dr. S. Ilic Introduction
28.10. No Lecture Dr. S. Ilic  
04.11. Camera Models Dr. S. Ilic Camera Models
11.11. Pose Estimation from 2D-3D correspondences (DLT, PnP?), RANSAC Dr. S. Ilic The same slides as in the previous lecture.
18.11. Rotation Parameterization Dr. S. Ilic Non-linear optimization
25.11. Non-linear optimization, robust estimation Dr. S. Ilic The same slides as in the previous lecture.
02.12. Convolutions, Filtering, Edge Detection Dr. S. Ilic Filtering and edge detection
09.12. Feature detection and description Dr. S. Ilic Feature detectors
Feature descriptors
16.12. Keypoint recognition, Random Forests Dr. S. Ilic Keypoint recognition, Random Forest
23.12. No Lecture Dr. S. Ilic  
13.01. Deep Learning Dr. S. Ilic  
20.01. Deep Learning Dr. S. Ilic  
27.01. Object Detection with Deep Learning Dr. S. Ilic  
03.02. Object Detection and Pose Estimation    

Exercise Projects Schedule

If you have questions about a specific exercise, please direct them to the responsible tutor. At the due date the results of the projects will be presented by each group of students followed by the questions of the tutors. The students should be ready to run the code after their presentation as well as show parts of the code and be prepared to answer the questions about their implementation.

Date Topic Conducted by Material/Literature Sample Output
17.10. No Tutorial      
24.10. Introduction to Tutorials Adrian Haarbach? MATLAB Introduction  
31.10. No Tutorial      
07.11. Exercise: Camera Transformations. Homework 1.1: Keypoint based object detection, pose estimation and refinement (Model preparation and Pose Estimation with PnP? and RANSAC) Yehya Abouelnaga, Ivan Pavlov and Adrian Haarbach Exercise 1
Solution-Exercise 1
Homework 1.1
Homework 1.1 - Data
Homework 1.1 - Code
 
14.11. Exercise: Camera Transformations repetition. Homework 1.1. Q&A Yehya Abouelnaga, Ivan Pavlov and Adrian Haarbach Exercise 1
Solution-Exercise 2
 
21.11. Exercise: Non-linear optimization and rotation parameterization. Homework 1.2 Yehya Abouelnaga, Ivan Pavlov and Adrian Haarbach Exercise 3
Solution-Exercise 3
Homework 1.1
Homework 1.2 - Data
Homework 1.2 - Code
 
28.11 Grading Session Homework 1.1      
05.12. Q&A for Homework 1.2 Adrian Haarbach?, Ivan Shugurov, Ivan Pavlov Midterm example
Solutions-midterm
 
09.12. Q&A for Homework 1.2 (after the lecture at 18:30h) Adrian Haarbach?, Roman Kaskman, Ivan Pavlov    
12.12. Grading Session Homework 1.2 Adrian Haarbach?, Ivan Shugurov, Ivan Pavlov, Roman Kaskman    
19.12. Exercise: Feature points. Random Forest. Homework 2 Mahdi Hamad, Sricharan Chiruvolu, Roman Kaskman    
09.01 Grading Session Homework 2 Roman Kaskman, Ivan Shugurov, Mai Bui?    
16.01 Exercise: Deep Learning basics; Homework 3 Agnieszka Tomczak, Ivan Shugurov, Sergey Zakharov    
23.01. Q&A Homework 3 Agnieszka Tomczak, Ivan Shugurov, Sergey Zakharov    
30.01. Q&A Homework 3 Agnieszka Tomczak, Ivan Shugurov, Sergey Zakharov    
06.02. Grading Session Homework 3 Agnieszka Tomczak, Ivan Shugurov, Sergey Zakharov    

TeachingForm
Title: Tracking and Detection in Computer Vision
Professor: Dr. Slobodan Ilic
Tutors: Mahdi Hamad, Agnieszka Tomczak,Ivan Shugurov, Mai Bui
Type: Lecture
Information: 2 + 4; 7 ECTS
Term: 2019WiSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.39 - 10 Dec 2019 - 22:47 - SlobodanIlic

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