This paper proposes a multi-modal situational interaction model for collaborative agents by fusing multisensorial information in a Multi-Agent Hierarchical Dynamic Bayesian Network (MAH-DBN) framework. The proposed model is learned in a data-driven methodology to estimate the states of interacting agents only from video sequences. This can be regarded as a two-fold methodology for improving visual-based localization and interaction between autonomous agents. In the learning stage, the odometry model is used to drive the video learning model for a robust localization and interaction modeling. During the testing phase, the learned Multi-Agent Hierarchical DBN (MAH-DBN) model is used for the localization of collaborative agents only from video sequences by proposing an inference method called Multi-Agent Coupled Markov Jump Particle Filter (MAC-MJPF).

A Data-Driven Approach for the Localization of Interacting Agents via a Multi-Modal Dynamic Bayesian Network Framework

Abrham Shiferaw;Giulia Slavic;Hafsa Iqbal;Lucio Marcenaro;Carlo Regazzoni
2022-01-01

Abstract

This paper proposes a multi-modal situational interaction model for collaborative agents by fusing multisensorial information in a Multi-Agent Hierarchical Dynamic Bayesian Network (MAH-DBN) framework. The proposed model is learned in a data-driven methodology to estimate the states of interacting agents only from video sequences. This can be regarded as a two-fold methodology for improving visual-based localization and interaction between autonomous agents. In the learning stage, the odometry model is used to drive the video learning model for a robust localization and interaction modeling. During the testing phase, the learned Multi-Agent Hierarchical DBN (MAH-DBN) model is used for the localization of collaborative agents only from video sequences by proposing an inference method called Multi-Agent Coupled Markov Jump Particle Filter (MAC-MJPF).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1102878
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