论文标题
PointTrackNet：用于从点云的3-D对象检测和跟踪的端到端网络
PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds
论文作者
论文摘要
最近基于机器学习的多对象跟踪（MOT）框架在3-D点云中变得流行。大多数传统的跟踪方法都使用过滤器（例如Kalman滤波器或粒子过滤器）以时间顺序预测对象位置，但是它们容易受到极端运动条件的影响，例如突然制动和转动。在这封信中，我们提出了PointTrackNet，这是一个端到端的3-D对象检测和跟踪网络，以生成前景掩码，3-D边界框以及每个检测到的对象的点跟踪关联位移。网络仅作为输入两个相邻点云帧。 KITTI跟踪数据集的实验结果在最新的情况下显示出竞争成果，尤其是在不规则和快速变化的情况下。
Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds. Most traditional tracking approaches use filters (e.g., Kalman filter or particle filter) to predict object locations in a time sequence, however, they are vulnerable to extreme motion conditions, such as sudden braking and turning. In this letter, we propose PointTrackNet, an end-to-end 3-D object detection and tracking network, to generate foreground masks, 3-D bounding boxes, and point-wise tracking association displacements for each detected object. The network merely takes as input two adjacent point-cloud frames. Experimental results on the KITTI tracking dataset show competitive results over the state-of-the-arts, especially in the irregularly and rapidly changing scenarios.
