Abstract Online multi-object tracking needs to overcome the intrinsic detector deficiencies, e.g., missing detections, false alarms, and inaccurate detection responses, to grow multiple object trajectories without using future information. Various distractions exist during this growing process like background clutters, similar targets, and occlusions, which present a great challenge. We in this work propose a method for learning a distractor-aware discriminative model that can handle continuous missed and inaccurate detection problems due to the occlusion or the motion blur. To deal with target appearance variations, a relational attention learning mechanism is proposed to capture the distinctive target appearances by selectively aggregating features from history states with weights extracted from their appearance topological relationship. Based on the discrimination model, a multi-stage tracking pipeline is designed for automatic trajectory initialization,propagation, and termination. Extensive experimental analyses and comparisons demonstrate its state-of-the-art performance on widely used challenging MOT16 and MOT17 benchmarks. The source code of this work is released to facilitate further studies on the multi-object tracking problem. 1

中文翻译：

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用于在线多对象跟踪的干扰器感知判别学习

摘要 在线多目标跟踪需要克服检测器固有的缺陷，例如检测缺失、误报和检测响应不准确，以在不使用未来信息的情况下增加多个目标轨迹。在这个增长过程中存在各种干扰，如背景杂乱、相似目标和遮挡，这是一个巨大的挑战。我们在这项工作中提出了一种学习干扰物感知判别模型的方法，该模型可以处理由于遮挡或运动模糊而导致的连续遗漏和不准确检测问题。为了处理目标外观变化，提出了一种关系注意学习机制，通过从历史状态中选择性地聚合特征和从其外观拓扑关系中提取的权重来捕获独特的目标外观。基于判别模型，设计了多级跟踪流水线，用于自动轨迹初始化、传播和终止。广泛的实验分析和比较证明了其在广泛使用的具有挑战性的 MOT16 和 MOT17 基准测试中的最先进性能。发布这项工作的源代码，以方便对多目标跟踪问题的进一步研究。1