Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications. A key component of this task is represented by the inherently multi-modal nature of human paths which makes socially acceptable multiple futures when human interactions are involved. To this end, we propose a generative architecture for multi-future trajectory predictions based on Conditional Variational Recurrent Neural Networks (C-VRNNs). Conditioning mainly relies on prior belief maps, representing most likely moving directions and forcing the model to consider past observed dynamics in generating future positions. Human interactions are modelled with a graph-based attention mechanism enabling an online attentive hidden state refinement of the recurrent estimation. To corroborate our model, we perform extensive experiments on publicly-available datasets (e.g., ETH/UCY, Stanford Drone Dataset, STATS SportVU NBA, Intersection Drone Dataset and TrajNet++) and demonstrate its effectiveness in crowded scenes compared to several state-of-the-art methods.

AC-VRNN: Attentive Conditional-VRNN for multi-future trajectory prediction / Bertugli, A.; Calderara, S.; Coscia, P.; Ballan, L.; Cucchiara, R.. - In: COMPUTER VISION AND IMAGE UNDERSTANDING. - ISSN 1077-3142. - 210:(2021), p. 103245. [10.1016/j.cviu.2021.103245]

AC-VRNN: Attentive Conditional-VRNN for multi-future trajectory prediction

Bertugli A.;
2021-01-01

Abstract

Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications. A key component of this task is represented by the inherently multi-modal nature of human paths which makes socially acceptable multiple futures when human interactions are involved. To this end, we propose a generative architecture for multi-future trajectory predictions based on Conditional Variational Recurrent Neural Networks (C-VRNNs). Conditioning mainly relies on prior belief maps, representing most likely moving directions and forcing the model to consider past observed dynamics in generating future positions. Human interactions are modelled with a graph-based attention mechanism enabling an online attentive hidden state refinement of the recurrent estimation. To corroborate our model, we perform extensive experiments on publicly-available datasets (e.g., ETH/UCY, Stanford Drone Dataset, STATS SportVU NBA, Intersection Drone Dataset and TrajNet++) and demonstrate its effectiveness in crowded scenes compared to several state-of-the-art methods.
2021
Bertugli, A.; Calderara, S.; Coscia, P.; Ballan, L.; Cucchiara, R.
AC-VRNN: Attentive Conditional-VRNN for multi-future trajectory prediction / Bertugli, A.; Calderara, S.; Coscia, P.; Ballan, L.; Cucchiara, R.. - In: COMPUTER VISION AND IMAGE UNDERSTANDING. - ISSN 1077-3142. - 210:(2021), p. 103245. [10.1016/j.cviu.2021.103245]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/330678
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