[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 :
Computer science
Author, co-author :
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)
External co-authors :
yes
Language :
English
Title :
Anticipating Suspicious Actions using a Small Dataset of Action Templates
Publication date :
January 2018
Event name :
13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)
Event organizer :
http://visapp.visigrapp.org/
Event place :
Madeira, Portugal
Event date :
from 27-01-2018 to 29-01-2018
Audience :
International
Main work title :
13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)
Peer reviewed :
Peer reviewed
European Projects :
H2020 - 689947 - STARR - Decision SupporT and self-mAnagement system for stRoke survivoRs
FnR Project :
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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