M6. Video Analysis

The objective of this module is to present the main concepts and technologies that are necessary for image sequence 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: mono-camera video sequences, multi-camera and depth camera sequences. Both theoretical bases and algorithms will be studied. Main subjects will be motion segmentation, background subtraction, motion estimation both in 2D and 3D, tracking algorithms and model-based analysis. Higher level techniques such as gesture or action recognition and video retrieval will also be studied. Students will work on a project on traffic monitoring where they will apply the concepts learned in the course.
Project title: 
Road traffic monitoring

The goal of this project is to learn the basic concepts and techniques related to video sequences processing, mainly for surveillance applications. We will focus on video sequences from outdoor scenarios, with the application of traffic monitoring in mind. The main techniques of video processing will be applied in the context of video surveillance: moving object segmentation, motion estimation and compensation and video object tracking are basic components of many video processing systems. In a first stage, moving object segmentation will be tackled considering scenarios with static camera. Afterwards, camera motion will be considered. Tracking of the moving objects can be performed in both scenarios. The tracking result provides high level information that can be analysed for traffic monitoring. The learning objectives for the students are the use of pixel based statistical models (such as mixture of gaussians) for modeling a scene background and for moving object segmentation, the development of optical flow estimation methods for camera motion compensation, and techniques for object tracking (ranging from simple blob analysis to more complex techniques based on filtering and probabilistic data association). The performance of the developed techniques will be measured using standard metrics for video analysis.

Module lectures: 

 

Academic Year 2017-2018        
Week Date Time Lecture Lecturer University Building Room
1 Tue. Feb.20th  16:00 -18:00 Introduction to video analysis and tracking. Video Montse Pardàs UPC    
1 Thu. Feb.22nd 16:00 -18:00 Video segmentation Montse Pardàs/ Xavier Giró UPC    
1 Thu. Feb.22nd 18:00 - 19:00 Project Introduction

Javier Ruiz/ Xavier Giró

UPC    
               
2 Tue. Feb.27th 16:00 -18:00 Motion estimation (I) Ramon Morros UPC    
2 Thu. Mar.1st 16:00 -18:00 Motion estimation (II) Ramon Morros UPC    
2 Thu. Mar.1st 18:00 -19:00 Project follow-up

Javier Ruiz/ Xavier Giró

UPC    
               
3 Tue. Mar.6th 16:00 -18:00 Tracking. Introduction and Kalman Filters Ramon Morros UPC    
3 Thu. Mar.8th 16:00 -18:00 Particle Filters Ramon Morros UPC    
3 Thu. Mar.8th 18:00 -19:00 Project follow-up

Javier Ruiz/ Xavier Giró

UPC    
               
4 Tue. Mar.13th 16:00 -18:00 Tracking contours and Video Objects Montse Pardàs UPC    
4 Thu. Mar.15th 16:00 -18:00 Model Based Tracking Javier Ruiz UPC    
4 Thu. Mar.15th 18:00 -19:00 Project follow-up

Javier Ruiz/ Xavier Giró

UPC    
               
5 Tue. Mar.20th 16:00 -17:00 Recognition: Pose and Dynamic Gestures Josep Ramon Casas UPC    
5 Thu. Mar.22nd 16:00 -18:00 Recognition: Actions recognition and Video clarssification Xavier Giró UPC    
5 Thu. Mar.22nd 18:00 -19:00 Project follow-up

Javier Ruiz/ Xavier Giró

UPC    
               
      Easter Holidays (from Mar. 26th to Apr. 2nd)        
               
6 Tue. Apr.3rd   HOMEWORK        
6 Thu. Apr.5th   HOMEWORK        
               
7 Thu. Apr.12th 16:00 -19:00 Project presentations

Javier Ruiz/ Xavier Giró

UPC    
               
8 Tue. Apr.17th   HOMEWORK        
Thu. Apr.19th   HOMEWORK        
               
9 Tue. Apr.26th 16:00 -19:00 EXAM Montse Pardàs UPC    
               

M6 Student Guide [here]