[en] In this paper, we propose to detect an action as soon as possible and ideally before it is fully completed. The objective is to support the monitoring of surveillance videos for preventing criminal or terrorist attacks. For such a scenario, it is of importance to have not only high detection and recognition rates but also low time latency for the detection. Our solution consists in an adaptive sliding window approach in an online manner, which efficiently rejects irrelevant data. Furthermore, we exploit both spatial and temporal information by constructing feature vectors based on temporal blocks. For an added efficiency, only partial template actions are considered for the detection. The relationship between the template size and latency is experimentally evaluated. We show promising preliminary experimental results using Motion Capture data with a skeleton representation of the human body.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
BAPTISTA, Renato ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Antunes, Michel
AOUADA, Djamila ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Anticipating Suspicious Actions using a Small Dataset of Action Templates
Date de publication/diffusion :
janvier 2018
Nom de la manifestation :
13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)
Organisateur de la manifestation :
http://visapp.visigrapp.org/
Lieu de la manifestation :
Madeira, Portugal
Date de la manifestation :
from 27-01-2018 to 29-01-2018
Manifestation à portée :
International
Titre de l'ouvrage principal :
13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)
Peer reviewed :
Peer reviewed
Projet européen :
H2020 - 689947 - STARR - Decision SupporT and self-mAnagement system for stRoke survivoRs
Projet FnR :
FNR10415355 - 3d Action Recognition Using Refinement And Invariance Strategies For Reliable Surveillance, 2015 (01/06/2016-31/05/2019) - Bjorn Ottersten
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