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

Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a robust MC-MOT sys- tem, however, is still highly challenging due to many practi- cal issues such as inconsistent lighting conditions, varying object movement patterns, or the trajectory occlusions of the objects between the cameras. To address these prob- lems, this work, therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP) approach 1 to solve the data association task. Compared to existing methods, our new model offers several advantages, including bet- ter feature representations and the ability to recover from lost tracks during camera transitions. Moreover, our model works gracefully regardless of the overlapping ratios be- tween the cameras. Experimental results show that we out- perform existing MC-MOT algorithms by a large margin on several practical datasets. Notably, our model works favor- ably on online settings but can be extended to an incremen- tal approach for large-scale datasets.