论文标题
TripletTrack：使用Triplet Embeddings和LSTM进行3D对象跟踪
TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM
论文作者
论文摘要
3D对象跟踪是自主驾驶系统中的关键任务。它对系统对周围环境的认识起着至关重要的作用。同时，对仅依赖廉价传感器（例如相机）的自动驾驶汽车算法引起了人们的兴趣。在本文中，我们研究了三胞胎嵌入的使用与3D对象跟踪的运动表示结合。我们从现成的3D对象检测器开始，并应用跟踪机制，其中对象与本地对象功能嵌入和运动描述符上计算的亲和力分数匹配。对特征嵌入式进行了训练，以包括有关视觉外观和单眼3D对象特征的信息，而运动描述符提供了对象轨迹的强烈表示。我们将证明我们的方法可以有效地重新识别对象，并且在闭塞情况下可靠，准确地表现出可靠，准确的表现，并且可以检测到不同视图领域的重新表现。实验评估表明，我们的方法的表现超过了努斯曲的最先进的余量。我们还获得了Kitti的竞争结果。
3D object tracking is a critical task in autonomous driving systems. It plays an essential role for the system's awareness about the surrounding environment. At the same time there is an increasing interest in algorithms for autonomous cars that solely rely on inexpensive sensors, such as cameras. In this paper we investigate the use of triplet embeddings in combination with motion representations for 3D object tracking. We start from an off-the-shelf 3D object detector, and apply a tracking mechanism where objects are matched by an affinity score computed on local object feature embeddings and motion descriptors. The feature embeddings are trained to include information about the visual appearance and monocular 3D object characteristics, while motion descriptors provide a strong representation of object trajectories. We will show that our approach effectively re-identifies objects, and also behaves reliably and accurately in case of occlusions, missed detections and can detect re-appearance across different field of views. Experimental evaluation shows that our approach outperforms state-of-the-art on nuScenes by a large margin. We also obtain competitive results on KITTI.
