论文标题
自我生长的空间图网络，用于上下文感知的行人轨迹预测
Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Prediction
论文作者
论文摘要
行人轨迹预测是一个活跃的研究领域，最近进行的工作将行人社交互动的准确模型及其上下文遵守嵌入到动态空间图中。但是，现有作品依赖于有关场景和动态的空间假设，这对在线系统中未知环境中的图表结构进行了重大挑战。此外，关系建模对预测性能的影响缺乏评估方法。为了填补这一空白，我们建议社交轨迹推荐门控的图形复发邻域网络（STR-GGRNN），该网络使用数据驱动的自适应在线邻里建议，基于上下文场景特征和行人视觉提示。通过在线非负矩阵分解（NMF）来实现邻里建议，以构建图形邻接矩阵，以预测行人的轨迹。基于广泛使用的数据集的实验表明，我们的方法的表现优于最新方法。我们表现最好的型号在ETH-UCY数据集上实现了12厘米ADE和$ \ sim $ 15 cm的FDE。每帧总共进行20K未来轨迹时，提出的方法仅需0.49秒。
Pedestrian trajectory prediction is an active research area with recent works undertaken to embed accurate models of pedestrians social interactions and their contextual compliance into dynamic spatial graphs. However, existing works rely on spatial assumptions about the scene and dynamics, which entails a significant challenge to adapt the graph structure in unknown environments for an online system. In addition, there is a lack of assessment approach for the relational modeling impact on prediction performance. To fill this gap, we propose Social Trajectory Recommender-Gated Graph Recurrent Neighborhood Network, (STR-GGRNN), which uses data-driven adaptive online neighborhood recommendation based on the contextual scene features and pedestrian visual cues. The neighborhood recommendation is achieved by online Nonnegative Matrix Factorization (NMF) to construct the graph adjacency matrices for predicting the pedestrians' trajectories. Experiments based on widely-used datasets show that our method outperforms the state-of-the-art. Our best performing model achieves 12 cm ADE and $\sim$15 cm FDE on ETH-UCY dataset. The proposed method takes only 0.49 seconds when sampling a total of 20K future trajectories per frame.
