Degree | Type | Year | Semester |
---|---|---|---|
4314099 Computer Vision | OB | 0 | 2 |
Module Coordinator: Dr. Javier Ruiz Hidalgo
The objective of this module is to present the main concepts and technologies that are necessary for video analysis. In the first place, we will present the applications of image sequence analysis and the different kind of data where these techniques will be applied, together with a general overview of the signal processing techniques and the general deep learning architectures in which video analysis is based. Examples will be given for mono-camera video sequences, multi-camera and depth camera sequences. Both theoretical bases and algorithms will be studied. For each subject, classical state of the art techniques will be presented, together with the deep learning techniques which lead to different approaches. Main subjects will be video segmentation, background subtraction, motion estimation, tracking algorithms and model-based analysis. Higher level techniques such as gesture or action recognition, deep video generation and cross-modal deep learning will also be studied. Students will work on a project on road traffic monitoring applied to ADAS (Advanced Driver Assistance Systems) where they will apply the concepts learned in the course. The project will focus on video object detection and segmentation, optical flow estimation and multi-target / multi-camera tracking of vehicles.
Supervised sessions:
Directed sessions:
Autonomous work:
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Lecture sessions | 20 | 0.8 | 4, 5, 9 |
Type: Supervised | |||
Project follow-up sessions | 8 | 0.32 | 1, 8, 4, 5, 6, 7, 3, 2, 9, 10 |
Type: Autonomous | |||
Homework | 113 | 4.52 | 1, 8, 4, 5, 6, 7, 3, 2, 9, 10 |
The final marks for this module will be computed with the following formula:
Final Mark = 0.4 x Exam + 0.55 x Project+ 0.05 x Attendance
where,
Exam: is the mark obtained in the Module Exam (must be >= 3)
Attendance: is the mark derived from the control of attendance at lectures (minimum 70%)
Projects: is the mark provided by the project coordinator based on the weekly follow- up of the project and deliverables. All accordingly with specific criteria such as:
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Exam | 0.4 | 2.5 | 0.1 | 1, 8, 7, 3, 2, 9, 10 |
Project | 0.55 | 6 | 0.24 | 1, 8, 4, 5, 6, 7, 3, 2, 9, 10 |
Session attendance | 0.05 | 0.5 | 0.02 | 1, 4, 5, 7, 9 |
Journal articles:
Books: