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

arXiv: https://arxiv.org/abs/1803.06579 · HTML: https://mlsanity.com/paper/1803.06579/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2018.

## Built on by (most cited first, 2 of 2)

- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [Measuring Novelty in Autonomous Vehicles Motion Using Local Outlier Factor Algorithm](https://mlsanity.com/paper/2104.11970/) (2021)

## Abstract (arXiv)

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.