Multi-Object Tracking MOT encompasses various tracking scenarios, each
characterized by unique traits. Effective trackers should demonstrate a high
degree of generalizability across diverse scenarios. However, existing trackers
struggle to accommodate all aspects or necessitate hypothesis and
experimentation to customize the association information motion and or
appearance for a given scenario, leading to narrowly tailored solutions with
limited generalizability. In this paper, we investigate the factors that
influence trackers generalization to different scenarios and concretize them
into a set of tracking scenario attributes to guide the design of more
generalizable trackers. Furthermore, we propose a point-wise to instance-wise
relation framework for MOT, i.e., GeneralTrack, which can generalize across
diverse scenarios while eliminating the need to balance motion and appearance.
Thanks to its superior generalizability, our proposed GeneralTrack achieves
state-of-the-art performance on multiple benchmarks and demonstrates the
potential for domain generalization.
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多目标跟踪应具备高度的一般化能力，然而现有的跟踪器往往无法满足各种特征，而通过研究并将其抽象成一组跟踪场景属性，我们提出了一种点对点到实例关系的跟踪框架 GeneralTrack，能够在不需要平衡运动和外观的情况下，高效地进行多场景跟踪，从而在多个基准测试中达到最先进的性能，并展示了领域泛化的潜力。