Authors:
Rensso Mora Colque
1
;
Carlos Caetano
1
;
Victor C. de Melo
1
;
Guillermo Camara Chavez
2
and
William Robson Schwartz
1
Affiliations:
1
Universidade Federal de Minas Gerais and DCC, Brazil
;
2
Universidade Federal de Ouro Preto and ICEB, Brazil
Keyword(s):
Anomalous Event Detection, Human-object Interaction, Contextual Information.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
Abstract:
This study proposes a novel approach to anomalous event detection that collects information from a specific context and is flexible enough to work in different scenes (i.e., the camera does need to be at the same location or in the same scene for the learning and test stages of anomaly event detection), making our approach able to learn normal patterns (i.e., patterns that do not entail an anomaly) from one scene and be employed in another as long as it is within the same context. For instance, our approach can learn the normal behavior for a context such the office environment by \emph{watching} a particular office, and then it can monitor the behavior in another office, without being constrained to aspects such as camera location, optical flow or trajectories, as required by the current works. Our paradigm shift anomalous event detection approach exploits human-object interactions to learn normal behavior patterns from a specific context. Such patterns are used afterwards to detect
anomalous events in a different scene. The proof of concept shown in the experimental results demonstrate the viability of two strategies that exploit this novel paradigm to perform anomaly detection.
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