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Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

CVPR 2020 · Graph learning › Knowledge and temporal graphs · approach: Spatio-temporal graph traffic forecasting · 1,000 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace recurrent trajectory modeling and feature aggregation with a spatio-temporal graph model and temporal convolutionsSocial-STGCNN · ETH/UCY AVGADE: 0.45 -> 0.44Helped
Replace recurrent trajectory modeling and feature aggregation with a spatio-temporal graph model and temporal convolutionsSocial-STGCNN · ETH/UCY AVGFDE: 0.94 -> 0.75Helped
Use one ST-GCNN layer and five TXP-CNN layers (ablation)Social-STGCNN · ETH/UCYADE / FDE: 0.44 / 0.75Helped
Increase the number of ST-GCNN layers from one to three, with five TXP-CNN layers (ablation)Social-STGCNN · ETH/UCYADE / FDE: 0.44 / 0.75 -> 0.54 / 0.93Hurt
Increase the number of ST-GCNN layers from one to five, with five TXP-CNN layers (ablation)Social-STGCNN · ETH/UCYADE / FDE: 0.44 / 0.75 -> 0.59 / 1.02Hurt
Increase the number of ST-GCNN layers from one to seven, with five TXP-CNN layers (ablation)Social-STGCNN · ETH/UCYADE / FDE: 0.44 / 0.75 -> 0.62 / 1.07Hurt
Use seven rather than five TXP-CNN layers while keeping one ST-GCNN layer (ablation)Social-STGCNN · ETH/UCYADE / FDE: 0.44 / 0.75 -> 0.48 / 0.87Hurt
Replace the proposed similarity kernel with an L2-distance kernel (ablation)Social-STGCNN · ETH/UCYADE / FDE: 0.44 / 0.75 -> 0.48 / 0.84Mixed
Replace the proposed similarity kernel with an exponential distance kernel (ablation)Social-STGCNN · ETH/UCYADE / FDE: 0.44 / 0.75 -> 0.50 / 0.84Mixed
Replace the proposed similarity kernel with inverse distance plus epsilon (ablation)Social-STGCNN · ETH/UCYADE / FDE: 0.44 / 0.75 -> 0.48 / 0.88Mixed
Set all adjacency values between different nodes to one instead of using the proposed kernel (ablation)Social-STGCNN · ETH/UCYADE / FDE: 0.44 / 0.75 -> 0.49 / 0.79Mixed

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