论文标题
Flowmot：3D多对象跟踪按场景流量关联
FlowMOT: 3D Multi-Object Tracking by Scene Flow Association
论文作者
论文摘要
大多数端到端的多对象跟踪（MOT）方法面临着低精度和概括能力差的问题。尽管传统的基于过滤器的方法可以取得更好的结果，但是它们很难被赋予最佳的超参数，并且在不同的情况下常常失败。为了减轻这些缺点，我们提出了一个名为FlowMot的基于激光雷达的3D MOT框架，该框架将点运动信息与传统匹配算法集成在一起，从而增强了运动预测的鲁棒性。首先，我们利用场景流估计网络在两个相邻帧之间获得隐式运动信息，并在上一个帧中计算每个旧曲目的预测检测。然后，我们使用匈牙利算法来生成与ID传播策略的最佳匹配关系，以完成跟踪任务。 Kitti MOT数据集的实验表明，我们的方法的表现优于最新的端到端方法，并通过最新的基于滤波器的方法实现了竞争性能。此外，我们的方法可以在基于过滤器方法失败的各种速度方案中稳步工作。
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.
