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A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS

Citation Author(s):
Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni
Submitted by:
Lucio Marcenaro
Last updated:
18 April 2018 - 10:40am
Document Type:
Presentation Slides
Document Year:
2018
Event:
Presenters:
Lucio Marcenaro
Paper Code:
SS-L2.5
 

This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations. Both observation types are used to characterize agents’ motion in a given environment. The proposed method generates locally uniform motion models by dividing a Gaussian process that approximates agents’ displacements on the scene and provides a Shared Level (SL) self-awareness based on Environment Centered (EC) models. Such models are then used to train in a semi-unsupervised way a set of Generative Adversarial Networks (GANs) that produce an estimation of external and internal parameters of moving agents. Obtained results exemplify the feasibility of using multi-perspective data for predicting and analyzing trajectory information.

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