Identifying Data 2020/21
Subject (*) Human Action Recognition Code 614535006
Study programme
Máster Universitario en Visión por Computador
Descriptors Cycle Period Year Type Credits
Official Master's Degree 2nd four-month period
First Optional 3
Language
English
Teaching method Hybrid
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Coordinador
Ortega Hortas, Marcos
E-mail
m.ortega@udc.es
Lecturers
Barreira Rodriguez, Noelia
Ortega Hortas, Marcos
E-mail
noelia.barreira@udc.es
m.ortega@udc.es
Web
General description
Contingency plan 1. Modificacións nos contidos
Ningunha.

2. Metodoloxías
*Metodoloxías docentes que se manteñen
Todas.

*Metodoloxías docentes que se modifican
En caso de necesidade, todas as metodoloxías empregadas poderían aplicarse de modo non presencial coas ferramentas dispoñibles (Moodle, Teams, etc.)

3. Mecanismos de atención personalizada ao alumnado
Atención continuada en Teams, Moodle e correo electrónico.

4. Modificacións na avaliación
Non son necesarias.

*Observacións de avaliación:
Ningunha.

5. Modificacións da bibliografía ou webgrafía
Ningunha.

Study programme competencies
Code Study programme competences
A2 CE2 - To know and apply machine learning and pattern recognition techniques applied to computer vision
A3 CE3 - To know and apply the concepts, methodologies and technologies of image and video analysis
A4 CE4 - To conceive, develop and evaluate complex computer vision systems
A9 CE9 - To know and apply the concepts, methodologies and technologies for the recognition of visual patterns in real scenes
B3 CB8 - That students are able to integrate knowledge and deal with the complexity of making judgements based on information that is incomplete or limited, including reflections on social and ethical responsibilities linked to the application of their knowledge and judgements
B7 CG2 - Ability to analyze a company's needs in the field of computer vision and determine the best technological solution for it
B11 CG6 - Ability to identify theoretical results or new technologies with innovative potential and convert them into products and services useful to society
B12 CG7 - Ability to learn autonomously for specialization in one or more fields of study
C3 CT3 - Development of the innovative and entrepreneurial spirit

Learning aims
Learning outcomes Study programme competences
Knowledge of recognition techniques applied to the recognition of people, and body parts. AC2
AC3
AC4
AC9
BC3
BC7
BC11
BC12
CC3
Analysis and evaluation of human action recognition applications AC2
AC3
AC4
AC9
BC3
BC7
BC11
BC12
CC3
Development of tools based on advanced technologies for recognition of human actions AC2
AC3
AC4
AC9
BC3
BC7
BC11
BC12
CC3

Contents
Topic Sub-topic
Detection and tracking of people.
Detection and monitoring of faces, extremities, and other features of interest.
Recognition of postural and behavioral patterns.
Applications of the recognition of human actions.

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Laboratory practice A9 A4 B3 6 21 27
Supervised projects B11 B7 C3 4 12 16
Guest lecture / keynote speech A3 A2 B12 11 18 29
 
Personalized attention 3 0 3
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Laboratory practice Practice in computer classrooms, learning based on the resolution of practical cases, combining work and autonomous learning with group work for cooperative learning
Supervised projects Realization of presentations of project-oriented work
Guest lecture / keynote speech Participatory master classes

Personalized attention
Methodologies
Supervised projects
Laboratory practice
Description

Attention to the challenges that are exposed to the students both in the practices and in the works exposed to us.

Assessment
Methodologies Competencies Description Qualification
Guest lecture / keynote speech A3 A2 B12 Demonstration of application of knowledge taught in class 30
Supervised projects B11 B7 C3 Practical projects related to the subject 40
Laboratory practice A9 A4 B3 Applied case development practices 30
 
Assessment comments

Sources of information
Basic

I.-O. Stathopoulou, G.A. Tsihrintzis. "Visual Affect Recognition", IOS Press, 2010. ISBN:978-I-60750-596-9.

Premaratne, P. "Human Computer Interaction Using Hand Gestures". Springer 2014. ISBN: 978-981-4585-68-2.

Gong, S.; Xiang, T. "Visual Analysis of Behaviour: From pixels to semantics". Springer 2011. ISBN: 978-0-85729-669-6.

Moeslund, T.B.; Hilton, A.; Krüger, V.; Sigal, L. (Eds.), "Visual Analysis of Humans: Looking at people". Springer, 2011. ISBN: 978-0-85729-996-3.

Salah, A.A.; Gevers, T. (Eds.), "Computer Analysis of Human Behavior". Springer, 2011. ISBN: 978-0-85729-993-2.

Murino, V.; Cristani, M.; Shah, S.; Savarese, S. "Group and Crowd Behavior for Computer Vision". 2017. ISBN: 9780128092767.

Complementary


Recommendations
Subjects that it is recommended to have taken before
Fundamentals of Machine Learning for Computer Vision /614535007
Fundamentals of Image Processing and Analysis /614535001
Image Description and Modeling/614535004

Subjects that are recommended to be taken simultaneously
Visual Recognition/614535005

Subjects that continue the syllabus

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.