CS623: Multimedia Surveillance Systems
Semester II, 2019-20

 

Course Information Lectures/Calendar Quizzes Labs

Course Information

Lectures (CS2): Thu - 9:00 AM, Fri - 9:00 AM
Labs (Lab 2): TBA

Objectives

Objective of the course is to give an overview of is to give an overview of statistical and machine learning techniques used for automatic surveillance using multimedia data. This course is designed to provide hands-on experience of the techniques with real-world data.

Outcomes

By taking this course, the students will be able to find answer to the following questions:
  • What is anomaly?
  • How to detect anomaly in a time series data?
  • What are different statistical and machine learning techniques to do surveillance in Multimedia data?

    • Prerequisite

    Multimedia Systems (CS507) OR Digital Image Processing and Analysis (CS517) OR Computer Vision (CS517) OR Image processing and pattern recognition (EE484).

    Course Requirements

    Student are required to attend two lectures per week and appear in two exams. In addition, there will be weekly lab sessions. During lab sessions, the students are required to solve and implement programming assignments.

    Grading Policy

    There will be lab exercises, quizzes, a mid-semester exam, a final exam and project. The tentative marks distribution is as follows:
    Quizzes (top 2 out of 3): 10%
    Lab Exercises (top n-1): 20%
    Mid-semester exam: 20%
    Final exam: 25%
    Project: 25%
    A student must score at least 40% marks to pass the course.

    Attendance Requirement

    The course will follow institute policy on attendance, i.e., the students should attend at least 75% of the lectures to get non F grade. If your attendance is above 90%, you will get 1 bonus mark. The same policy applies to attendance in the lab sessions.

    Code of Ethics & Professional Responsibility

    It is expected that students who are taking this course will demonstrate a keen interest in learning and not mere fulfilling the requirement towards their degree. Discussions that help the student understand a concept or a problem is encouraged. However, each student must turn in original work. Plagiarism/copying of any form, will be dealt with strict disciplinary action. This involves, copying from the internet, textbooks and any other material for which you do not own the copyright. Copying part of the code will be considered plagiarism. Lending the code to others will be considered plagiarism too, for it is difficult to investigate who copied whose code. Students who violate this policy will directly receive a failing grade in the course. Remember - Your partial submission can fetch you some points, but submitting other's work as your own can result in you failing the course. Please talk to the instructor if you have questions about this policy. All academic integrity issues will be handled in accordance with institute regulations.

    Textbooks

    Primary Textbook

    There is no single textbook for the course. We will rely heavily on the web sources for the content. Few possible reference books are given below:

    Reference Books

    1. Outlier analysis, Authors: Charu C. Aggarwal, Publisher: Springer, Cham, 2015.
    2. Anomaly Detection for Monitoring, Authors: Baron Schwartz and Preetam Jinka, Publisher: O'Reilly Media, Inc., Year 2016. [Link].
    3. Multimedia video-based surveillance systems: Requirements, Issues and Solutions, Authors: Foresti, Gian Luca, Petri Mähönen, and Carlo S. Regazzoni, Publisher: Vol. 573. Springer Science & Business Media, 2012.

    Language/Tools

    For lab exercises we will primarily use Python. For projects, students are free to use any language.

    Teaching Assistant

    Pratibha Kumari (2017csz0006@iitrpr.ac.in)

    Contact Me

    By appointment at
    Room No. 319, S. Ramanujan Block, Permanent Campus, IIT Ropar

    Tentative Topics

    • Anomaly modelling using statistical techniques - Clustering, Mixture Models
    • Anomaly detection using Machine Learning Techniques - Classifiers, Generative Adversarial Networks, Auto-encoders
    • Advanced topics - forgery, authentication
    • Case Studies

    Quizzes

    Quiz 1 - TBA
    Quiz 2 - TBA
    Quiz 3 - TBA

    Projects

    Projects are to be done individually or in a group of two. The end application of the project should be in one of the following application areas: Surveillance, Safety, Health, Education, Agriculture, Sports, Public Transportation. Project requirements:
    • The reports must be prepafed in ACM Multimedia LaTeX format. Good quality English is expected in the report.
    • The code should be submitted through GitHub or Bitbucket repository. You can make a private repository and show me with your login. I will observe the activities on repository (commits, etc.) to check the progress.
    • Dataset can be submitted through Pen Drive of Google Drive.
    • You are free to use resources (code) available on the Internet with proper references. However, during evaluation you need to explicitly mention parts with your work.
    • There will be marks for creativity in the project.
    • There will be 4 to 5 evaluations of the project.

    Lab Exercises

    There will be in-lab sessions every week. The labs will alternate between graded lab and practice lab. Practice labs will be guided labs where the TA will walk you through a few example problems. At the end you will be given a set of practice problems. During the graded lab, you will be asked to implement a variation/adaptation of one or more practice problems. You have to attend at least 75% of the labs to get a non F grade. Those who attend more than 90% labs will get one bonus mark.

    Lectures and Calendar

    Lectures Dates Topics Readings Events
    L1 Jan 9 Introduction
    L2 Jan 10 Anomaly Introduction Chapter 1 (Outlier Analysis) Lab1
    L3-4 Jan 16-17 KNN, Clustering Chapter 1 (Outlier Analysis), Anomaly Detection: A Review
    L5-6-7 Jan 24-25, Jan 27 GMM Chapter 2 (Outlier Analysis), Anomaly Detection: A Review
    L8 Jan 29 Anomaly Detection using Histogram, Linear Regression Chapter 2 (Outlier Analysis), Anomaly Detection: A Review
    L9-10 Feb 6-7 Anomaly Detection with PCA Chapter 3.3 (Outlier Analysis), PCA Tutorial
    L11-14 Feb 13-14, 19-20 Anomaly Detection using Source Coding and One Class SVM Chapter 3.4 (Outlier Analysis), Bishop, Christopher M. Pattern recognition and machine learning, SVM Tutorial
    L15-16 Mar 3-5 Autoencoders Goodfella Chapter 14
    L17-18 Mar 18-19 GANs Goodfella Chapter 20
    L17-18 Mar 25-26 Generative Adversarial Networks Goodfella Chapter 20, Paper Tutorial, Video Tutorial/td>

    *This is a tentative schedule. The schedule can change according to the need at the discretion of the instructor.

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