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.

本文探讨了使用三元组嵌入和运动表示相结合的方法，对三维物体进行跟踪，通过实验证明该方法有效地再识别物体，并且可以在遮挡，漏检和不同场景中准确地检测再次出现。

TripletTrack：使用三元组嵌入和 LSTM 的 3D 对象跟踪

TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM

In this paper we tackle the problem of vehicle re-identification in a camera
network utilizing triplet embeddings. Re-identification is the problem of
matching appearances of objects across different cameras. With the
proliferation of surveillance cameras enabling smart and safer cities, there is
an ever-increasing need to re-identify vehicles across cameras. Typical
challenges arising in smart city scenarios include variations of viewpoints,
illumination and self occlusions. Most successful approaches for
re-identification involve (deep) learning an embedding space such that the
vehicles of same identities are projected closer to one another, compared to
the vehicles representing different identities. Popular loss functions for
learning an embedding (space) include contrastive or triplet loss. In this
paper we provide an extensive evaluation of these losses applied to vehicle
re-identification and demonstrate that using the best practices for learning
embeddings outperform most of the previous approaches proposed in the vehicle
re-identification literature. Compared to most existing state-of-the-art
approaches, our approach is simpler and more straightforward for training
utilizing only identity-level annotations, along with one of the smallest
published embedding dimensions for efficient inference. Furthermore in this
work we introduce a formal evaluation of a triplet sampling variant (batch
sample) into the re-identification literature.

本文利用三元组嵌入解决了摄像头网络中车辆重新识别的问题，通过学习一个嵌入空间并使用三元组 Loss 函数提高了识别正确率。