The global multi-object tracking (MOT) system can consider interaction,
occlusion, and other ``visual blur'' scenarios to ensure effective object
tracking in long videos. Among them, graph-based tracking-by-detection
paradigms achieve surprising performance. However, their fully-connected nature
poses storage space requirements that challenge algorithm handling long videos.
Currently, commonly used methods are still generated trajectories by building
one-forward associations across frames. Such matches produced under the
guidance of first-order similarity information may not be optimal from a
longer-time perspective. Moreover, they often lack an end-to-end scheme for
correcting mismatches. This paper proposes the Composite Node Message Passing
Network (CoNo-Link), a multi-scene generalized framework for modeling
ultra-long frames information for association. CoNo-Link's solution is a
low-storage overhead method for building constrained connected graphs. In
addition to the previous method of treating objects as nodes, the network
innovatively treats object trajectories as nodes for information interaction,
improving the graph neural network's feature representation capability.
Specifically, we formulate the graph-building problem as a top-k selection task
for some reliable objects or trajectories. Our model can learn better
predictions on longer-time scales by adding composite nodes. As a result, our
method outperforms the state-of-the-art in several commonly used datasets.

该研究论文提出了一种名为 CoNo-Link 的综合节点信息传递网络，用于建模超长帧信息以进行多目标跟踪，通过将对象轨迹视为节点进行信息交互，改进了图神经网络的特征表示能力，并在多个常用数据集中表现出色。

多场景广义轨迹全局图解算器结合复合节点用于多物体跟踪

Multi-Scene Generalized Trajectory Global Graph Solver with Composite  Nodes for Multiple Object Tracking

This paper introduces Click to Move (C2M), a novel framework for video
generation where the user can control the motion of the synthesized video
through mouse clicks specifying simple object trajectories of the key objects
in the scene. Our model receives as input an initial frame, its corresponding
segmentation map and the sparse motion vectors encoding the input provided by
the user. It outputs a plausible video sequence starting from the given frame
and with a motion that is consistent with user input. Notably, our proposed
deep architecture incorporates a Graph Convolution Network (GCN) modelling the
movements of all the objects in the scene in a holistic manner and effectively
combining the sparse user motion information and image features. Experimental
results show that C2M outperforms existing methods on two publicly available
datasets, thus demonstrating the effectiveness of our GCN framework at
modelling object interactions. The source code is publicly available at
this https URL

该研究介绍了一个名为 Click to Move (C2M) 的新框架，可以通过简单的鼠标轨迹点控制合成视频的运动轨迹，并组合用户提供的运动信息和图像特征的 GCN 框架，从而在两个公开数据集上优于现有方法。

点击移动：使用稀疏动作控制视频生成

Click to Move: Controlling Video Generation with Sparse Motion

The main challenge of online multi-object tracking is to reliably associate
object trajectories with detections in each video frame based on their tracking
history. In this work, we propose the Recurrent Autoregressive Network (RAN), a
temporal generative modeling framework to characterize the appearance and
motion dynamics of multiple objects over time. The RAN couples an external
memory and an internal memory. The external memory explicitly stores previous
inputs of each trajectory in a time window, while the internal memory learns to
summarize long-term tracking history and associate detections by processing the
external memory. We conduct experiments on the MOT 2015 and 2016 datasets to
demonstrate the robustness of our tracking method in highly crowded and
occluded scenes. Our method achieves top-ranked results on the two benchmarks.

本文提出了一种循环自回归网络（RAN），它是一种时间生成建模框架，可以可靠地将物体轨迹与每个视频帧中的检测相关联，并通过内、外存储器来处理多个物体的外貌和运动动态。实验证明，我们的追踪方法在高度拥挤和遮挡的场景中具有鲁棒性，并在 MOT2015 和 2016 数据集上取得了排名最高的结果。