  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.
