Online two-dimensional (2D) multi-object tracking (MOT) is a challenging task when the objects of interest have similar appearances. In that case, the motion of objects is another helpful cue for tracking and discriminating multiple objects. However, when using a single moving camera for online 2D MOT, observable motion cues are contaminated by global camera movements and, thus, are not always predictable. To deal with unexpected camera motion, we propose a new data association method that effectively exploits structural constraints in the presence of large camera motion. In addition, to reduce incorrect associations with mis-detections and false positives, we develop a novel event aggregation method to integrate assignment costs computed by structural constraints. We also utilize structural constraints to track missing objects when they are re-detected again. By doing this, identities of the missing objects can be retained continuously. Experimental results validated the effectiveness of the proposed data association algorithm under unexpected camera motions. In addition, tracking results on a large number of benchmark datasets demonstrated that the proposed MOT algorithm performs robustly and favorably against various online methods in terms of several quantitative metrics, and that its performance is comparable to offline methods.

中文翻译：

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用于在线多目标跟踪的结构约束数据关联

当感兴趣的对象具有相似的外观时，在线二维 (2D) 多对象跟踪 (MOT) 是一项具有挑战性的任务。在这种情况下，物体的运动是跟踪和区分多个物体的另一个有用线索。然而，当使用单个移动摄像机进行在线 2D MOT 时，可观察到的运动线索会受到全局摄像机移动的污染，因此并不总是可预测的。为了处理意外的相机运动，我们提出了一种新的数据关联方法，可以在存在大相机运动的情况下有效地利用结构约束。此外，为了减少与误检测和误报的不正确关联，我们开发了一种新的事件聚合方法来整合由结构约束计算的分配成本。当它们再次被重新检测时，我们还利用结构约束来跟踪丢失的对象。通过这样做，可以连续保留丢失对象的身份。实验结果验证了所提出的数据关联算法在意外相机运动下的有效性。此外，在大量基准数据集上的跟踪结果表明，所提出的 MOT 算法在几个定量指标方面对各种在线方法具有鲁棒性和优势，并且其性能可与离线方法相媲美。