scholarly journals Space-Time Social Relationship Pooling Pedestrian Trajectory Prediction Model

2020 ◽  
Vol 32 (12) ◽  
pp. 1918-1925
Author(s):  
Lin Mao ◽  
Xinfei Gong ◽  
Dawei Yang ◽  
Rubo Zhang
2021 ◽  
Vol 3 ◽  
Author(s):  
Uwe Dick ◽  
Maryam Tavakol ◽  
Ulf Brefeld

We present a data-driven model that rates actions of the player in soccer with respect to their contribution to ball possession phases. This study approach consists of two interconnected parts: (i) a trajectory prediction model that is learned from real tracking data and predicts movements of players and (ii) a prediction model for the outcome of a ball possession phase. Interactions between players and a ball are captured by a graph recurrent neural network (GRNN) and we show empirically that the network reliably predicts both, player trajectories as well as outcomes of ball possession phases. We derive a set of aggregated performance indicators to compare players with respect to. to their contribution to the success of their team.


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