- Course
- 2023-2024
- Semester
- 2
- ECTS
- 3
- Type
- Elective
- University
- UDC
Subject objectives
-Knowledge of recognition techniques applied to the recognition of people, and body parts.
-Analysis and evaluation of human action recognition applications
-Development of tools based on advanced technologies for recognition of human actions
Contents
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.
Basic and complementary bibliography
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.
Competencies
CE2 – To know and apply machine learning and pattern recognition techniques applied to
computer vision
CE3 – To know and apply the concepts, methodologies and technologies of image and video
analysis
CE4 – To conceive, develop and evaluate complex computer vision systems
CE9 – To know and apply the concepts, methodologies and technologies for the recognition of
visual patterns in real scenes
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
CG2 – Ability to analyze a company’s needs in the field of computer vision and determine the
best technological solution for it
G6 – Ability to identify theoretical results or new technologies with innovative potential and
convert them into products and services useful to society
CG7 – Ability to learn autonomously for specialization in one or more fields of study
CT3 – Development of the innovative and entrepreneurial spirit
Teaching methodology
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
Evaluation system
-Guest lecture / keynote speech (30):
Demonstration of application knowledge taught in class
Competences: CE2,CE3 CG7
-Supervised projects (40) :
Practical projects related to the subject
Competences: CG2,CG3 CT3
-Laboratory practice (30):
Applied case development practices
Competences: CE4,CE9 CB8
Studying time and personal work
Recommended study time for students is about 2 hours per week. Additionally, we estimate that they should spend about 3 hours / week working in a number of assignments. All of these activities add up to around 60h/semester.
Subject study recommendations
Subjects that it is recommended to have taken before:
Fundamentals of Machine Learning for Computer Vision /614535007
Fundamentals of Image Analysis and Processing/614535001
Image Description and Modeling/614535004
Subjects that are recommended to be taken simultaneously:
Visual Recognition/614535005