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.
翻译:多数端到端多目标跟踪(MOT)方法面临低精度和简化能力差的问题。尽管传统的过滤法可以取得更好的结果,但它们很难被赋予最佳的超参数,而且往往在各种情况下都失败。为了减轻这些弊端,我们提议了一个基于LiDAR的3DMOT框架,名为FlowMOT,它将点向运动信息与传统的匹配算法相结合,增强运动预测的稳健性。我们首先利用现场流估计网络获得两个相邻框架之间的隐性运动信息,并计算前一个框架中每个旧轨道的预测探测结果。然后我们使用匈牙利算法来产生与ID传播战略的最佳匹配关系,以完成跟踪任务。关于KITTI MOT数据集的实验表明,我们的方法超越了最近的端到端方法,并实现了与基于过滤法的先进方法的竞争性性能。此外,我们还可以在过滤法可能失败的各种速度假设中稳步工作。