论文标题
DetFlowTrack：基于对象检测和场景流估计的3D多对象跟踪
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation
论文作者
论文摘要
3D多对象跟踪（MOT）是无人车感觉模块的重要组成部分。大多数方法都独立优化对象检测和数据关联。这些方法使网络结构变得复杂，并限制了MOT准确性的提高。我们提出了一个基于对象检测和场景流估计的同时优化的3D MOT框架。在框架中，提出了一个检测引导场景流量模块，以缓解不正确的框架间关联问题。对于更准确的场景流标记，尤其是在旋转的运动中，提出了基于盒子变换的场景流真相计算方法。 Kitti MOT数据集的实验结果显示了最新的竞争结果以及在旋转时极端运动下的鲁棒性。
3D Multi-Object Tracking (MOT) is an important part of the unmanned vehicle perception module. Most methods optimize object detection and data association independently. These methods make the network structure complicated and limit the improvement of MOT accuracy. we proposed a 3D MOT framework based on simultaneous optimization of object detection and scene flow estimation. In the framework, a detection-guidance scene flow module is proposed to relieve the problem of incorrect inter-frame assocation. For more accurate scene flow label especially in the case of motion with rotation, a box-transformation-based scene flow ground truth calculation method is proposed. Experimental results on the KITTI MOT dataset show competitive results over the state-of-the-arts and the robustness under extreme motion with rotation.
