Co-MOT: Exploring the Collaborative Relations in Traffic Flow for 3D Multi-Object Tracking

In real-world scenes, vehicles and pedestrians on the road often exhibit consistency in their overall motion, forming the traffic flow we observe. Exploring this global collective motion consistency to aid in 3D multi-object tracking (MOT) tasks is an under-investigated issue in existing research. Recently, Graph Neural Networks (GNN) have been introduced to model interactions between targets in 3D tracking problems, achieving remarkable performance. However, existing GNN based methods usually employ neighborhood-based approaches to construct graphs which are unable to fully exploit collective relations in traffic flow. In this paper, we propose a GNN based 3D MOT method which effectively utilizes the collective motion consistency in traffic flow. Collective motion is modeled with a densely connected intra-flow graph within the collective group, allowing information to flow quickly. To build the intra-flow graph, we propose an effective collinearity condition to distinguish potential collective groups from the detected objects. For reasoning on the graph, we propose a progressive serial message-passing solver which enables the network to learn complex group movement relationships based on a thorough understanding of simple neighborhood relations. Our proposed method achieves state-of-the-art performance on public datasets: NuScenes and KITTI tracking benchmark. We have conducted extensive experiments to evaluate the comprehensive performance of our proposed method which demonstrates the effectiveness of the proposed method.