All students must validate 30 ECTS per semester.We offer two speciality tracks:

Track 1: Vision and Applications
The first track focuses on various applications of computer vision: biomedical applications, people detection, object tracking, computational photography.

Track 2: Vision and devices
The second track focuses on devices to capture images (intelligent sensors, medical imaging systems) and to visualise and interact with them (augmented reality)




Course name: Vision for Multiple or Moving Cameras Credits: 6

  • Class type: Theory+Practice
  • Hours per week: 2+1
  • Type of the exam: 50% Lab assigments, 50% theory exam


The aim of this course is to help the student understand the fundamentals behind collaborative multi-camera video analysis, which is nowadays the main practical way to confront many challenging computer vision scenarios.

UNIT I: The projected reality

  • Introduction to projection
  • Fundamentals of Projective Geometry
  • Calibration of a single camera
  • Binocular vision

UNIT II: Detection, description and matching of reference points

  • Pyramids and scale-space theory.
  • Detection and description of feature points: theory and methods.
  • Comparison and searching strategies for the matching of local descriptions.

UNIT III: Analysis in multi-camera scenarios

  • Calibration in camera networks
  • Collaborative objects detection
  • Multi-target tracking

Recommended reading:

  • R. Hartley, A. Zisserman, “Multiple view geometry in computer vision”, Cambridge UP, 2003
  • T. Lindeberg, “Scale-Space Theory”, Kluwer Academic Publishers, Boston, MA, 1997.
  • Awad A., Hassaballah M. (eds) “Image Feature Detectors and Descriptors. Studies in Computational Intelligence”, vol 630. Springer, Cham
  • O. Javed, M. Shah, "Automated Multi-camera Surveillance: Algorithms and Practice", Springer 2008 H. Aghajan, and A. Cavallaro, eds. Multi-camera networks: principles and applications. Academic press, 2009.
  • S. Gong, M. Cristani, S. Yan and C. Loy, eds. Person Re-identification. Springer, 2014.
  • P. Spagnolo,  P. Mazzeo, and C. Distante. Human behavior understanding in networked sensing. Springer, 2014.
  • Lecturer (name, position, degree): Marcos Escudero-Vinolo, Ph.D., Assistant Professor
  • Additional lecturers, if exist(name, position, degree): Juan Carlos San Miguel, Ph.D., Assistant Professor; Jesús Bescós, Ph.D., Associate Professor

The European Credit Transfer and Accumulation System (ECTS) is a student-centred system based on the student workload required to achieve the objectives of a programme of study. Its aim is to facilitate the recognition of study periods undertaken abroad by mobile students through the transfer credits. The ECTS is based on the principle that 60 credits are equivalent to the workload of full-time student during one academic year.