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2021 | OriginalPaper | Chapter

SURVANT: An Innovative Semantics-Based Surveillance Video Archives Investigation Assistant

Authors : Giuseppe Vella, Anastasios Dimou, David Gutierrez-Perez, Daniele Toti, Tommaso Nicoletti, Ernesto La Mattina, Francesco Grassi, Andrea Ciapetti, Michael McElligott, Nauman Shahid, Petros Daras

Published in: Pattern Recognition. ICPR International Workshops and Challenges

Publisher: Springer International Publishing

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Abstract

SURVANT is an innovative video archive investigation system that aims to drastically reduce the time required to examine large amounts of video content. It can collect the videos relevant to a specific case from heterogeneous repositories in a seamless manner. SURVANT employs Deep Learning technologies to extract inter/intra-camera video analytics, including object recognition, inter/intra-camera tracking, and activity detection. The identified entities are semantically indexed enabling search and retrieval of visual characteristics. Semantic reasoning and inference mechanisms based on visual concepts and spatio-temporal metadata allows users to identify hidden correlations and discard outliers. SURVANT offers the user a unified GIS-based search interface to unearth the required information using natural language query expressions and a plethora of filtering options. An intuitive interface with a relaxed learning curve assists the user to create specific queries and receive accurate results using advanced visual analytics tools. GDPR compliant management of personal data collected from surveillance videos is integrated in the system design.

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Title: SURVANT: An Innovative Semantics-Based Surveillance Video Archives Investigation Assistant
Authors: Giuseppe Vella
Anastasios Dimou
David Gutierrez-Perez
Daniele Toti
Tommaso Nicoletti
Ernesto La Mattina
Francesco Grassi
Andrea Ciapetti
Michael McElligott
Nauman Shahid
Petros Daras
Publisher: Springer International Publishing
Book: Pattern Recognition. ICPR International Workshops and Challenges
Print ISBN: 978-3-030-68786-1

Electronic ISBN: 978-3-030-68787-8

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-68787-8_44

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