Examination arrangement

Course content

In this course we will discuss state of the art video analysis for video understanding and its applications in different domains; e.g. video surveillance and video guided surgery. Actual topics may include but are not limited to the following topics:

• Video indexing, summarization, and retrieval.
• Video-based object classification.
• Audio and video semantic analysis.
• Object detection and tracking.
• Video processing in the compressed domain.
• Multi-camera systems and multi-camera data fusion and processing.
• Objective video quality evaluation.
• 3D and multi-view video compression.
• Deep learning for medical image processing
• Deep learning for video surveillance

Learning outcome

Having completed the course, the students will - Possess advanced knowledge within the area of intelligent video technology, with emphasis on representing, analyzing, compressing and processing video. - Possess specialized insight and good understanding of the research frontier in selected topics of video analysis especially of relevance to video surveillance, video-based navigation and video guided surgery applications.

Skills and general competence: - Be able to use relevant and suitable methods when carrying out further research and development activities in the area of video analysis and processing - Be able to critically review relevant literature when solving the assigned problem or topic. - Be able to give a presentation and demonstrate their findings.

Learning methods and activities

-E-learning and Seminars:

Additional information: -Lectures by the course instructors and guest lectures by invited experts. Student presentations on selected topics.

E-learning material will be available for this course: PDF of the lectures and student presentations, and possibly audio/video recordings of the lectures will be provided. These E-lectures material will be available on an e-Learning platform (Blackboard). Which will also be used for discussions with the teachers and between the students.

Compulsory requirements: -Oral presentations. Each student needs to study one topic, make a short introductory presentation (5min) about it and later give a deeper presentation (20-30min) and write a report about the work done and its outcomes.

Further on evaluation

Re-sit: One re-sit for the Oral re-examination may be offered for valid reasons only, needs to have given the presentation/implementation and report accepted to be allowed for the re-sit.

Forms of assessment: - 20-30 min Oral Exam, individually (counts 100%, evaluated by lecturers and external reviewer) / video conference via Skype for distance students may be arranged - Topic report (is a pre-requisit to take the exam and is evaluated by lecturers as pass/fail). - Each part must be individually approved of.

Specific conditions

Recommended previous knowledge

Good knowledge in machine learning, image and video processing or analysis.

Required previous knowledge

Machine learning and image/video processing and analysis or equivalent courses

Course materials

Recent research papers and book chapters from various books. Material will be published on the course pages before the start of the course.

Credit reductions