623.915 (21S) Current Topics in Multimedia Systems: Video Search with Deep Learning

Sommersemester 2021

Registration deadline has expired.

First course session
08.03.2021 10:00 - 12:00 online Off Campus
... no further dates known

Overview

Due to the COVID-19 pandemic, it may be necessary to make changes to courses and examinations at short notice (e.g. cancellation of attendance-based courses and switching to online examinations).

For further information regarding teaching on campus, please visit: https://www.aau.at/en/corona.
Lecturer
Course title german Current Topics in Multimedia Systems: Video Search with Deep Learning
Type Lecture - Course (continuous assessment course )
Course model Online course
Hours per Week 2.0
ECTS credits 4.0
Registrations 16 (20 max.)
Organisational unit
Language of instruction English
possible language(s) of the assessment German , English
Course begins on 08.03.2021
eLearning Go to Moodle course

Time and place

Please note that the currently displayed dates may be subject to change due to COVID-19 measures.
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Course Information

Intended learning outcomes

  • Methoden und Möglichkeiten der automatischen Videoanalyse verstehen
  • Methoden von Videosuchsystemen verstehen  
  • Methoden des Machine Learnings überblicken und für Videoanalyse einsetzen 
  • Einsatzszenarien der Videoanalyse und Videosuche benennen und erläutern
  • Geeignete Inhaltsanalysemethoden für Videos erklären und anwenden
  • Deep Learning mit CNNs im Detail verstehen und anwenden
  • Verschiedene Deep Learning Modelle verstehen und für visuelle Analyse anwenden 
  • Geeignete Benutzerschnittstellen für Video-Interaktion und Suche kennen
  • Die Leistung eines Videosuchsystems beurteilen können

Teaching methodology including the use of eLearning tools

  • Vortrag
  • Übungen
  • Projekt

Course content

  • What is Video Analysis and Retrieval
  • Components of a Video Content Search System
  • Computer Vision with OpenCV 
  • Visual Content Segmentation and Clustering
  • Deep Learning with Convolutional Neural Networks (CNNs)
  • AlexNet, GoogLeNet, ResNet
  • Fully Convolutional Neural Networks (Fast-R-CNN, Faster-R-CNN, ...)
  • Interactive Video Search
  • Evaluation of Video Retrieval Systems

Intended learning outcomes

  • Understand methods and possibilities of video content analysis
  • Understand methods of video content search systems
  • Know machine learning methods and their use for visual content analysis 
  • Know and describe usage scenarios of video analysis and video search/retrieval  
  • Explain appropriate content analysis methods for videos and use them in practice
  • Understand deep learning with CNNs in detail and use them in practice
  • Know different kinds of deep learning models and apply them to practical problems
  • Know appropriate methods and aspects of user interfaces for video interaction  and apply them in practice
  • Evaluate the system performance of a video search system 

Teaching methodology including the use of eLearning tools

  • Lecture
  • Assignments
  • Project

Course content

  • What is Video Analysis and Retrieval
  • Components of a Video Content Search System
  • Computer Vision with OpenCV 
  • Visual Content Segmentation and Clustering
  • Deep Learning with Convolutional Neural Networks (CNNs)
  • AlexNet, GoogLeNet, ResNet
  • Fully Convolutional Neural Networks (Fast-R-CNN, Faster-R-CNN, ...)
  • Interactive Video Search
  • Evaluation of Video Retrieval Systems

Examination information

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Examination methodology

Abschlussprojekt oder mündliche Prüfung

Examination topic(s)

Inhalte der Folien, Übungen und Projekte

Assessment criteria / Standards of assessment for examinations

Erreichte Punkte 

Examination methodology

project or oral exam

Examination topic(s)

content of slides, exercises/assignments and projects

Assessment criteria / Standards of assessment for examinations

achieved points

Grading scheme

Grade / Grade grading scheme

Position in the curriculum

  • Master's degree programme Applied Informatics (SKZ: 911, Version: 13W.1)
    • Subject: Distributed Multimedia Systems (Compulsory elective)
      • Current Topics in Distributed Multimedia Systems: Multimedia Information Retrieval ( 2.0h VK / 4.0 ECTS)
        • 623.915 Current Topics in Multimedia Systems: Video Search with Deep Learning (2.0h VC / 4.0 ECTS)
  • Master's degree programme Informatics (SKZ: 911, Version: 19W.2)
    • Subject: Multimedia Systems (Compulsory elective)
      • Weitere LVen aus dem gewählten Spezialisierungsfach ( 0.0h XX / 12.0 ECTS)
        • 623.915 Current Topics in Multimedia Systems: Video Search with Deep Learning (2.0h VC / 4.0 ECTS)
          Absolvierung im 1., 2. Semester empfohlen

Equivalent courses for counting the examination attempts

Sommersemester 2024
  • 623.915 VC Image and Video Analysis with Deep Learning (2.0h / 4.0ECTS)
Sommersemester 2023
  • 623.915 VC Image and Video Analysis with Deep Learning (2.0h / 4.0ECTS)
Sommersemester 2022
  • 623.915 VC Current Topics in Multimedia Systems: Video Search with Deep Learning (2.0h / 4.0ECTS)
Sommersemester 2020
  • 623.915 VC Current Topics in Multimedia Systems: Content Search with Deep Learning (2.0h / 4.0ECTS)
Sommersemester 2019
  • 623.915 VC Current Topics in Distributed Multimedia Systems: Video Analysis & Retrieval (2.0h / 4.0ECTS)
Sommersemester 2018
  • 623.915 VC Current Topics in Distributed Multimedia Systems: Video Retrieval (2.0h / 4.0ECTS)
Wintersemester 2015/16
  • 623.915 VC Current Topics in Distributed Multimedia Systems: Video Retrieval (2.0h / 4.0ECTS)