论文标题
在交通场景中进行多个对象跟踪的监督和无监督的检测：比较研究
Supervised and Unsupervised Detections for Multiple Object Tracking in Traffic Scenes: A Comparative Study
论文作者
论文摘要
在本文中，我们提出了一个称为MF-tracker的多个对象跟踪器，该跟踪器在其跟踪框架中集成了多个经典特征（空间距离和颜色）以及现代特征（检测标签和重新识别功能）。由于我们的跟踪器可以处理来自无监督和监督的对象探测器的检测，因此我们还研究了在我们的方法中监督和无监督的检测输入的影响，并通常跟踪道路使用者。我们还将我们的结果与在UA-Detrac和UrbanTracker数据集上应用的现有方法进行了比较。结果表明，我们所提出的方法在两个具有不同输入的数据集中表现良好（MOTA范围从0：3491到0：5805，对于UrbanTracker数据集上的无监督输入，在不同情况下，在UA Detrac DataSet上，对UrbanTracker数据集的无监督输入和0：7638的平均MOTA为0：7638）。训练有素的监督对象探测器可以在具有挑战性的情况下提供更好的结果。但是，在更简单的情况下，如果没有良好的培训数据，则无监督的方法可以表现良好，并且可以是一个很好的选择。
In this paper, we propose a multiple object tracker, called MF-Tracker, that integrates multiple classical features (spatial distances and colours) and modern features (detection labels and re-identification features) in its tracking framework. Since our tracker can work with detections coming either from unsupervised and supervised object detectors, we also investigated the impact of supervised and unsupervised detection inputs in our method and for tracking road users in general. We also compared our results with existing methods that were applied on the UA-Detrac and the UrbanTracker datasets. Results show that our proposed method is performing very well in both datasets with different inputs (MOTA ranging from 0:3491 to 0:5805 for unsupervised inputs on the UrbanTracker dataset and an average MOTA of 0:7638 for supervised inputs on the UA Detrac dataset) under different circumstances. A well-trained supervised object detector can give better results in challenging scenarios. However, in simpler scenarios, if good training data is not available, unsupervised method can perform well and can be a good alternative.
