Previous online 3D Multi-Object Tracking(3DMOT) methods terminate a tracklet
when it is not associated with new detections for a few frames. But if an
object just goes dark, like being temporarily occluded by other objects or
simply getting out of FOV, terminating a tracklet prematurely will result in an
identity switch. We reveal that premature tracklet termination is the main
cause of identity switches in modern 3DMOT systems. To address this, we propose
Immortal Tracker, a simple tracking system that utilizes trajectory prediction
to maintain tracklets for objects gone dark. We employ a simple Kalman filter
for trajectory prediction and preserve the tracklet by prediction when the
target is not visible. With this method, we can avoid 96% vehicle identity
switches resulting from premature tracklet termination. Without any learned
parameters, our method achieves a mismatch ratio at the 0.0001 level and
competitive MOTA for the vehicle class on the Waymo Open Dataset test set. Our
mismatch ratio is tens of times lower than any previously published method.
Similar results are reported on nuScenes. We believe the proposed Immortal
Tracker can offer a simple yet powerful solution for pushing the limit of
3DMOT. Our code is available at
this https URL

我们提出了 Immortal Tracker 方法，通过轨迹预测维持暂时被遮挡或超出拍摄范围的物体的轨迹，避免了 3DMOT 系统中因过早终止轨迹引起的身份切换问题。实验结果表明，此方法避免了 96% 因轨迹过快结束导致的车辆身份切换，而其不需要学习参数的特性还使其具有竞争优势。

不死追踪器：轨迹永存

Immortal Tracker: Tracklet Never Dies

Simple Online and Realtime Tracking (SORT) is a pragmatic approach to
multiple object tracking with a focus on simple, effective algorithms. In this
paper, we integrate appearance information to improve the performance of SORT.
Due to this extension we are able to track objects through longer periods of
occlusions, effectively reducing the number of identity switches. In spirit of
the original framework we place much of the computational complexity into an
offline pre-training stage where we learn a deep association metric on a
large-scale person re-identification dataset. During online application, we
establish measurement-to-track associations using nearest neighbor queries in
visual appearance space. Experimental evaluation shows that our extensions
reduce the number of identity switches by 45%, achieving overall competitive
performance at high frame rates.

文中介绍了一个基于 SORT 方法的多目标跟踪算法，通过整合外观信息能有效地降低物体遮挡时的身份切换次数，并在利用最近邻查询视觉外观空间建立测量轨迹关联时表现出竞争性的性能。