Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric environment. This is non-trivial because human motion is inherently multi-modal: given a history of human motion paths, there are many plausible ways by which people could move in the future. Additionally, people activities are often driven by goals, e.g. reaching particular locations or interacting with the environment. We address the aforementioned aspects by proposing a new recurrent generative model that considers both single agents' future goals and interactions between different agents. The model exploits a double attention-based graph neural network to collect information about the mutual influences among different agents and to integrate it with data about agents' possible future objectives. Our proposal is general enough to be applied to different scenarios: the model achieves state-of-the-art results in both urban environments and also in sports applications.

Dag-net: Double attentive graph neural network for trajectory forecasting / Monti, A.; Bertugli, A.; Calderara, S.; Cucchiara, R.. - (2020), pp. 2551-2558. (Intervento presentato al convegno 25th International Conference on Pattern Recognition, ICPR 2020 tenutosi a ita nel 2021) [10.1109/ICPR48806.2021.9412114].

Dag-net: Double attentive graph neural network for trajectory forecasting

Bertugli A.;
2020-01-01

Abstract

Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric environment. This is non-trivial because human motion is inherently multi-modal: given a history of human motion paths, there are many plausible ways by which people could move in the future. Additionally, people activities are often driven by goals, e.g. reaching particular locations or interacting with the environment. We address the aforementioned aspects by proposing a new recurrent generative model that considers both single agents' future goals and interactions between different agents. The model exploits a double attention-based graph neural network to collect information about the mutual influences among different agents and to integrate it with data about agents' possible future objectives. Our proposal is general enough to be applied to different scenarios: the model achieves state-of-the-art results in both urban environments and also in sports applications.
2020
Proceedings - International Conference on Pattern Recognition
10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
Institute of Electrical and Electronics Engineers Inc.
978-1-7281-8808-9
Monti, A.; Bertugli, A.; Calderara, S.; Cucchiara, R.
Dag-net: Double attentive graph neural network for trajectory forecasting / Monti, A.; Bertugli, A.; Calderara, S.; Cucchiara, R.. - (2020), pp. 2551-2558. (Intervento presentato al convegno 25th International Conference on Pattern Recognition, ICPR 2020 tenutosi a ita nel 2021) [10.1109/ICPR48806.2021.9412114].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/330682
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