CoMoGCN: Coherent Motion Aware Trajectory Prediction with Graph Representation
This addresses the problem of accurate group-wise motion prediction for applications like robot navigation and autonomous driving, offering an incremental improvement by incorporating crowd coherence.
The paper tackles trajectory prediction in crowded scenes by proposing CoMoGCN, which clusters pedestrians into groups based on motion coherence and uses graph convolutional networks with variational autoencoders, achieving state-of-the-art performance on multiple benchmarks.
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.