基于双重注意力时空图卷积网络的行人轨迹预测

向晓倩,陈璟

Pedestrian trajectory prediction based on dual-attention spatial-temporal graph convolutional network

Xiaoqian XIANG,Jing CHEN

表 4 本研究模型在不同组件下的消融实验结果（ADE/FDE）

Tab.4 Ablation experiment results (ADE/FDE) of propsed model in different components

组件 变体 ADE/FDE SDD ETH HOTEL UNIV ZARA1 ZARA2 平均值 Attention w/o 0.43/0.70 0.21/0.36 0.28/0.46 0.24/0.42 0.20/0.35 0.27/0.45 9.28/15.35 SAtt 0.40/0.68 0.19/0.35 0.23/0.40 0.19/0.35 0.14/0.27 0.23/0.41 9.20/15.28 TAtt 0.37/0.63 0.19/0.33 0.22/0.39 0.19/0.34 0.14/0.27 0.24/0.39 9.18/15.25 TAtt+SAtt 0.37/0.60 0.17/0.30 0.23/0.39 0.19/0.33 0.14/0.26 0.22/0.38 9.16/15.21 WeightA w/o 0.41/0.67 0.21/0.37 0.25/0.43 0.24/0.38 0.18/0.29 0.26/0.42 9.23/15.30 ${{A}_{{L_2}}}$ 0.39/0.65 0.19/0.36 0.25/0.41 0.21/0.36 0.15/0.27 0.24/0.41 9.18/15.25 ${\underline { A_t }} $ 0.37/0.60 0.17/0.30 0.23/0.39 0.19/0.33 0.14/0.26 0.22/0.38 9.16/15.21 Sampling random 0.62/1.10 0.42/0.64 0.47/0.85 0.34/0.50 0.30/0.49 0.43/0.71 9.30/15.58 $\underline{{\rm{purpose}}}$ 0.37/0.60 0.17/0.30 0.23/0.39 0.19/0.33 0.14/0.26 0.22/0.38 9.16/15.21 Multi-head w/o 0.41/0.67 0.17/0.30 0.23/0.39 0.19/0.34 0.14/0.25 0.23/0.39 9.20/15.25 2 0.36/0.60 0.18/0.33 0.23/0.39 0.19/0.35 0.14/0.26 0.22/0.39 9.18/15.24 $\underline 4$ 0.37/0.60 0.17/0.30 0.23/0.39 0.19/0.33 0.14/0.26 0.22/0.38 9.16/15.21 6 0.44/0.76 0.17/0.30 0.23/0.41 0.19/0.35 0.15/0.28 0.24/0.42 9.19/15.23 8 0.39/0.63 0.16/0.28 0.23/0.40 0.19/0.35 0.15/0.27 0.22/0.39 9.17/15.23 Loss ${{L}_1}$ 0.40/0.67 0.19/0.37 0.28/0.40 0.21/0.37 0.20/0.30 0.25/0.42 9.18/15.27 ${{L}_2}$ 0.39/0.65 0.20/0.36 0.24/0.43 0.23/0.35 0.17/0.27 0.24/0.41 9.17/15.25 $ \underline{ {{L}}_1 + {{L}}_2 } $ 0.37/0.60 0.17/0.30 0.23/0.39 0.19/0.33 0.14/0.26 0.22/0.38 9.16/15.21