Most state-of-the-art multiple-object tracking (MOT) methods adopt the tracking-by-detection (TBD) paradigm, which is a two-step procedure including the detection module and the tracking module. In these methods, the tracking performance heavily depends on initial detections and data association. In this paper, we present an online MOT method by introducing a single-object tracking (SOT) based on correlation filter. Our contributions lie in twofold: (a) we use the KCF-based SOT in learning of discriminative target appearance relying on handcrafted and deep features and (b) we employ the predicted result to refine the detection mistakes in a new way. Furthermore, we introduce normalize APCE score as an occlusion indicator of tracklet confidence, and build a candidate target hypotheses set to improve the association performance. Both approaches are found beneficial to eliminate the track errors caused by the inability of association algorithm. The experimental results, both qualitative and quantitative on three benchmark datasets, demonstrate that our tracking algorithm achieves comparable or even better results than competitor approaches.

中文翻译：

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使用基于 KCF 的单目标跟踪器和遮挡分析进行在线多目标跟踪

大多数最先进的多目标跟踪 (MOT) 方法采用逐检测跟踪 (TBD) 范式，这是一个包括检测模块和跟踪模块的两步过程。在这些方法中，跟踪性能在很大程度上取决于初始检测和数据关联。在本文中，我们通过引入基于相关滤波器的单目标跟踪 (SOT) 来提出一种在线 MOT 方法。我们的贡献有两个：（a）我们使用基于 KCF 的 SOT 来学习依赖手工和深度特征的判别目标外观；（b）我们使用预测结果以新的方式改进检测错误。此外，我们引入归一化 APCE 分数作为轨迹置信度的遮挡指标，并构建候选目标假设集以提高关联性能。发现这两种方法都有助于消除由于关联算法无法实现而导致的轨道误差。三个基准数据集的定性和定量实验结果表明，我们的跟踪算法实现了与竞争对手方法相当甚至更好的结果。