Report
A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents
| العنوان: | A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents |
|---|---|
| المؤلفون: | Baydoun, Mohamad, Ravanbakhsh, Mahdyar, Campo, Damian, Marin, Pablo, Martin, David, Marcenaro, Lucio, Cavallaro, Andrea, Regazzoni, Carlo S. |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2018 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1803.06579 |
| رقم الانضمام: | edsarx.1803.06579 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |