  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.
