A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents

Mohamad Baydoun; Mahdyar Ravanbakhsh; Damian Campo; Pablo Marin; David Martin; Lucio Marcenaro; Andrea Cavallaro; Carlo S. Regazzoni

arXiv · 1803.06579

A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents

Mohamad Baydoun·Mahdyar Ravanbakhsh·Damian Campo·Pablo Marin·David Martin·Lucio Marcenaro·Andrea Cavallaro·Carlo S. Regazzoni

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Abstract

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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Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni. 2018-03-17. A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents. https://arxiv.org/abs/1803.06579

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