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The global multi-object tracking (MOT) system can consider interaction,occlusion, and other ``visual blur'' scenarios to ensure effective objecttracking in long videos. Among them, graph-based tracking-by-detectionparadigms achieve surprising performance. However, their fully-connected natureposes storage space requirements that challenge algorithm handling long videos.Currently, commonly used methods are still generated trajectories by buildingone-forward associations across frames. Such matches produced under theguidance of first-order similarity information may not be optimal from alonger-time perspective. Moreover, they often lack an end-to-end scheme forcorrecting mismatches. This paper proposes the Composite Node Message PassingNetwork (CoNo-Link), a multi-scene generalized framework for modelingultra-long frames information for association. CoNo-Link's solution is alow-storage overhead method for building constrained connected graphs. Inaddition to the previous method of treating objects as nodes, the networkinnovatively 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 taskfor some reliable objects or trajectories. Our model can learn betterpredictions on longer-time scales by adding composite nodes. As a result, ourmethod outperforms the state-of-the-art in several commonly used datasets.

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