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Video analysis for Investigation of Criminal and TerrORIst Activities


VICTORIA Interface control document

Internal and external interfaces of VICTORIA: data objects, different type of interfaces calls, configuration files, etc. Description of how analytics modules can be added into VICTORIA

The VAFI community and marketplace

Platform hosting the VAFI community with marketplace functions as described in section 2.

Training curricula development and scheduling

Using a skill-based structure, the document will produce training curricula adapted to the end-users' needs in order to meet their training requirements

Training methodologies and evaluation criteria definition

In this deliverable will be produced the VICTORIA training methodologies for going beyond the current end users training programs and cover the end users’ training needs and targets.

VICTORIA Dissemination plan

Document describing the strategy and operational plan to raise awareness about the VICTORIA project.

Training contents and tools selection

This document will describe the produced training contents for the LEA members including contents on legal procedures, privacy and ethical principles. In addition, the selection of the developed tools for performing the training will be also shown.

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Having yes, using no? About the new legal regime for biometric data

Published in: ISSN 0267-3649
DOI: 10.1016/j.clsr.2017.11.004

Generic Object and Motion Analytics for Accelerating Video Analysis within VICTORIA

Author(s): D. Schreiber, M. Boyer, E. Broneder, A. Opitz, S. Veigl
Published in: 2018 European Intelligence and Security Informatics Conference, Issue 24-25 October 2018, 2018, Page(s) Page 86
DOI: 10.1109/EISIC.2018.00024

Large Scale Audio-Visual Video Analytics Platform for Forensic Investigations of Terroristic Attacks

Author(s): A. Schindler, M. Boyer, A. Lindley, D. Schreiber, T. Philpp
Published in: 2019 25th International Conference on MultiMedia Modeling, Issue -, 2019, Page(s) -

Visual Analytics for Semi-Automatic 4D Crime Scene Reconstruction

Author(s): N. Weiler, M. Kraus, T. Kilian, W. Jentner and D. A. Keim
Published in: 4th International Symposium on Big Data Visual and Immersive Analytics, 2018

Towards Accountability: The Formalization and Articulation of Fairness in Machine Learning

Author(s): Laurens Naudts
Published in: IFIP Summer School 2018, Issue 13th edition, 2018

The Articulation of Fair Machine Learning: Luck Equality and Group Differentiation

Author(s): Laurens Naudts
Published in: Amsterdam Privacy Conference, Issue 3rd edition, 2018, Page(s) 1-9 pages

Fair or Unfair Algorithmic Differentiation? Luck Egalitarianism as a lens for evaluating algorithmic decision-making

Author(s): Laurens Naudts
Published in: Data for Policy 2017, Issue 3rd edition, 2017, Page(s) 1-6

How Machine Learning Generates Unfair Inequalities and How Data Protection Instruments May Help in Mitigating Them

Author(s): Laurens Naudts
Published in: Data Protection and Privacy: The Internet of Bodies, 2018, Page(s) 304