论文标题
comogcn：连贯的运动意识轨迹预测与图表
CoMoGCN: Coherent Motion Aware Trajectory Prediction with Graph Representation
论文作者
论文摘要
预测人类轨迹对于机器人人群导航和自动驾驶等任务至关重要。对于准确的团体运动预测，建模社交互动至关重要。但是，大多数现有方法不考虑有关人群中连贯性的信息，而是仅考虑成对的交互。在这项工作中，我们提出了一个新颖的框架，连贯的运动意识图卷积网络（COMOGCN），以在具有组约束的拥挤场景中进行轨迹预测。首先，我们根据运动相干将行人轨迹群分组。然后，我们使用图形卷积网络有效地汇总了人群信息。 COMOGCN还利用变异自动编码器通过建模分布来捕获人类轨迹的多模式性质。我们的方法在几种不同的轨迹预测基准上实现了最先进的性能，并且在考虑所有基准测试中的最佳平均性能。
Forecasting human trajectories is critical for tasks such as robot crowd navigation and autonomous driving. Modeling social interactions is of great importance for accurate group-wise motion prediction. However, most existing methods do not consider information about coherence within the crowd, but rather only pairwise interactions. In this work, we propose a novel framework, coherent motion aware graph convolutional network (CoMoGCN), for trajectory prediction in crowded scenes with group constraints. First, we cluster pedestrian trajectories into groups according to motion coherence. Then, we use graph convolutional networks to aggregate crowd information efficiently. The CoMoGCN also takes advantage of variational autoencoders to capture the multimodal nature of the human trajectories by modeling the distribution. Our method achieves state-of-the-art performance on several different trajectory prediction benchmarks, and the best average performance among all benchmarks considered.
