论文标题
使用3D雷达立方体检测基于CNN的道路用户检测
CNN based Road User Detection using the 3D Radar Cube
论文作者
论文摘要
这封信提出了一种基于雷达的新型，单帧的多级检测方法（行人，骑自行车者，汽车），该方法利用了低级雷达立方体数据。该方法提供了雷达目标和对象级的类信息。雷达靶标在将目标特征扩展到其位置周围的3D雷达立方体的裁剪块后分别分别分类，从而捕获局部速度分布中运动部件的运动。为此步骤提出了卷积神经网络（CNN）。之后，对象建议是通过聚类步骤生成的，这不仅考虑了雷达目标的位置和速度，而且还考虑了其计算的类得分。在现实生活数据集的实验中，我们证明，我们的方法的表现优于目标和对象的最新方法，即平均达到0.70（基线：0.68）目标和0.56（基线：0.48）（0.48）对象 - 对象 - 对象 - f1得分。此外，我们研究了消融研究中使用的特征的重要性。
This letter presents a novel radar based, single-frame, multi-class detection method for moving road users (pedestrian, cyclist, car), which utilizes low-level radar cube data. The method provides class information both on the radar target- and object-level. Radar targets are classified individually after extending the target features with a cropped block of the 3D radar cube around their positions, thereby capturing the motion of moving parts in the local velocity distribution. A Convolutional Neural Network (CNN) is proposed for this classification step. Afterwards, object proposals are generated with a clustering step, which not only considers the radar targets' positions and velocities, but their calculated class scores as well. In experiments on a real-life dataset we demonstrate that our method outperforms the state-of-the-art methods both target- and object-wise by reaching an average of 0.70 (baseline: 0.68) target-wise and 0.56 (baseline: 0.48) object-wise F1 score. Furthermore, we examine the importance of the used features in an ablation study.
