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Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computervision problem due to its emerging applicability in several real-worldapplications. Despite a large number of existing works, solving the dataassociation problem in any MC-MOT pipeline is arguably one of the mostchallenging tasks. Developing a robust MC-MOT system, however, is still highlychallenging due to many practical issues such as inconsistent lightingconditions, varying object movement patterns, or the trajectory occlusions ofthe objects between the cameras. To address these problems, this work,therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP)approach to solve the data association task. Compared to existing methods, ournew model offers several advantages, including better feature representationsand the ability to recover from lost tracks during camera transitions.Moreover, our model works gracefully regardless of the overlapping ratiosbetween the cameras. Experimental results show that we outperform existingMC-MOT algorithms by a large margin on several practical datasets. Notably, ourmodel works favorably on online settings but can be extended to an incrementalapproach for large-scale datasets.

DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking