FlowMOT: 3D Multi-Object Tracking by Scene Flow Association
This work provides an incremental improvement in 3D multi-object tracking for autonomous driving systems by enhancing robustness in varying speed scenarios.
This paper addresses the limitations of existing 3D Multi-Object Tracking (MOT) methods by integrating point-wise motion information from scene flow estimation with a traditional matching algorithm. The proposed FlowMOT framework achieves competitive performance on the KITTI MOT dataset, outperforming recent end-to-end methods and working steadily in various-speed scenarios where filter-based methods may fail.
Most end-to-end Multi-Object Tracking (MOT) methods face the problems of low accuracy and poor generalization ability. Although traditional filter-based methods can achieve better results, they are difficult to be endowed with optimal hyperparameters and often fail in varying scenarios. To alleviate these drawbacks, we propose a LiDAR-based 3D MOT framework named FlowMOT, which integrates point-wise motion information with the traditional matching algorithm, enhancing the robustness of the motion prediction. We firstly utilize a scene flow estimation network to obtain implicit motion information between two adjacent frames and calculate the predicted detection for each old tracklet in the previous frame. Then we use Hungarian algorithm to generate optimal matching relations with the ID propagation strategy to finish the tracking task. Experiments on KITTI MOT dataset show that our approach outperforms recent end-to-end methods and achieves competitive performance with the state-of-the-art filter-based method. In addition, ours can work steadily in the various-speed scenarios where the filter-based methods may fail.