Applied Video Sequences Analysis

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


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

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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