Applied Video Sequences Analysis

Semester
Second semester: UAM, Madrid
Credit
6
Class Type
Practical, Theory
Type of the exam
25% theory exam, 75% Lab assignments
Prerequisites (if exist)
Lecturer
Juan Carlos San Miguel, Ph.D., Associate Professor
Hours per week
2+1

Content

The aim of this course is to train the student in the practical use of basic and state-of-the-art algorithms for the analysis of a video sequence. This course will be project-oriented, focused on handling these algorithms with the help of the OpenCV software library.

UNIT I: Foreground/Objects detection and segmentation

Background subtraction: parametric and non-parametric models
Shadow detection
Specific object detectors
UNIT II: Video object tracking

Template matching
Mean-shift tracking
Kalman and Particle Filters
Tracking by detection
UNIT III: Event detection and understanding

Definitions
Sparse scenarios
Crowded scenarios

Recommended reading

T. Moeslund, Introduction to video and image processing: Building real systems and applications. Springer Science & Business Media, 2012.
T. Bouwmans, F. Porikli, B. Hferlin, and A. Vacavant. Background Modeling and Foreground Detection for Video Surveillance. Chapman and Hall/CRC, 2014
E. Maggio, A. Cavallaro, Video Tracking: Theory and Practice, Wiley, 2011.
Fu, Yun, ed. Human Activity Recognition and Prediction. Springer, 2015.
Atrey, M. Kankanhalli, and A. Cavallaro, eds. Intelligent multimedia surveillance: current trends and research. Springer Science & Business Media, 2013.
A. Kaehler and G. Bradski. Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library. O’Reilly Media, Inc., 2016.

Additional lecturers, if exist(name, position, degree): Marcos Escudero-Vinolo, Ph.D., Associate Professor ; José M. Martínez, Ph.D., Full Professor

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