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Computer Science > Computer Vision and Pattern Recognition

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

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.
Comments: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2018
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:1803.06579 [cs.CV]
  (or arXiv:1803.06579v1 [cs.CV] for this version)

Submission history

From: Mahdyar Ravanbakhsh [view email]
[v1] Sat, 17 Mar 2018 22:00:08 GMT (6121kb,D)