Existing end-to-end Multi-Object Tracking (e2e-MOT) methods have not
surpassed non-end-to-end tracking-by-detection methods. One potential reason is
its label assignment strategy during training that consistently binds the
tracked objects with tracking queries and then assigns the few newborns to
detection queries. With one-to-one bipartite matching, such an assignment will
yield unbalanced training, i.e., scarce positive samples for detection queries,
especially for an enclosed scene, as the majority of the newborns come on stage
at the beginning of videos. Thus, e2e-MOT will be easier to yield a tracking
terminal without renewal or re-initialization, compared to other
tracking-by-detection methods. To alleviate this problem, we present Co-MOT, a
simple and effective method to facilitate e2e-MOT by a novel coopetition label
assignment with a shadow concept. Specifically, we add tracked objects to the
matching targets for detection queries when performing the label assignment for
training the intermediate decoders. For query initialization, we expand each
query by a set of shadow counterparts with limited disturbance to itself. With
extensive ablations, Co-MOT achieves superior performance without extra costs,
e.g., 69.4% HOTA on DanceTrack and 52.8% TETA on BDD100K. Impressively, Co-MOT
only requires 38\% FLOPs of MOTRv2 to attain a similar performance, resulting
in the 1.4$\times$ faster inference speed.

Co-MOT 是一种简单有效的方法，通过阴影概念的新颖合作竞争标签分配来促进端到端的多目标跟踪，使得标签分配策略更加平衡，以优越的性能获得追踪表现。

连接端到端和非端到端多目标跟踪之间的差距

Bridging the Gap Between End-to-end and Non-End-to-end Multi-Object  Tracking

Existing detection methods commonly use a parameterized bounding box (BBox)
to model and detect (horizontal) objects and an additional rotation angle
parameter is used for rotated objects. We argue that such a mechanism has
fundamental limitations in building an effective regression loss for rotation
detection, especially for high-precision detection with high IoU (e.g. 0.75).
Instead, we propose to model the rotated objects as Gaussian distributions. A
direct advantage is that our new regression loss regarding the distance between
two Gaussians e.g. Kullback-Leibler Divergence (KLD), can well align the actual
detection performance metric, which is not well addressed in existing methods.
Moreover, the two bottlenecks i.e. boundary discontinuity and square-like
problem also disappear. We also propose an efficient Gaussian metric-based
label assignment strategy to further boost the performance. Interestingly, by
analyzing the BBox parameters' gradients under our Gaussian-based KLD loss, we
show that these parameters are dynamically updated with interpretable physical
meaning, which help explain the effectiveness of our approach, especially for
high-precision detection. We extend our approach from 2-D to 3-D with a
tailored algorithm design to handle the heading estimation, and experimental
results on twelve public datasets (2-D/3-D, aerial/text/face images) with
various base detectors show its superiority.

本文提出用高斯分布模型来检测旋转物体，通过使用 Kullback-Leibler 分布测度作为新的回归损失函数，能够有效提高检测性能，同时提出一种高效的基于高斯度量的标签分配策略。实验结果表明，这种方法在二维和三维图像中的性能优于其他方法。

将旋转物体检测为高斯分布及其三维泛化

Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization

Recently, many arbitrary-oriented object detection (AOOD) methods have been
proposed and attracted widespread attention in many fields. However, most of
them are based on anchor-boxes or standard Gaussian heatmaps. Such label
assignment strategy may not only fail to reflect the shape and direction
characteristics of arbitrary-oriented objects, but also have high
parameter-tuning efforts. In this paper, a novel AOOD method called General
Gaussian Heatmap Label Assignment (GGHL) is proposed. Specifically, an
anchor-free object-adaptation label assignment (OLA) strategy is presented to
define the positive candidates based on two-dimensional (2-D) oriented Gaussian
heatmaps, which reflect the shape and direction features of arbitrary-oriented
objects. Based on OLA, an oriented-bounding-box (OBB) representation component
(ORC) is developed to indicate OBBs and adjust the Gaussian center prior
weights to fit the characteristics of different objects adaptively through
neural network learning. Moreover, a joint-optimization loss (JOL) with area
normalization and dynamic confidence weighting is designed to refine the
misalign optimal results of different subtasks. Extensive experiments on public
datasets demonstrate that the proposed GGHL improves the AOOD performance with
low parameter-tuning and time costs. Furthermore, it is generally applicable to
most AOOD methods to improve their performance including lightweight models on
embedded platforms.

本文提出了一种新的任意方向物体检测方法，称为 “广义高斯热力图标签分配法”，其中使用一种无锚盒对象适应标签分配策略，并开发了面向边界框的表示分量，设计了联合优化损失来改善 AOOD 性能。该方法适用于嵌入式平台上的轻型模型，具有低参数调整和时间成本。