Multi-target multi-camera tracking systems track many pedestrians through videos taken from multiple cameras. Generally, multi-target multi-camera tracking comprises three steps, namely, detection, feature extraction, and data association. It also involves a number of marginal post-processing procedures, such as pruning and interpolating. The task is a complicated and challenging problem. In this work, we mainly focus on the process of data association. When correlation clustering-based algorithms are adopted in data association, serious information loss may be observed, especially when pedestrians in a video run into occlusion. Thus, we propose a method called feature group which mitigates the decline in accuracy under occlusions. The proposed method is intuitional but easy to implement without changing the original framework. After comprehensive experiments, the proposed method is proved effective and is able to make substantial improvements on the DukeMTMC dataset. The feature group method is also competitive relative to other state-of-the-art methods.

多目标多摄像机跟踪系统通过从多个摄像机拍摄的视频来跟踪许多行人。通常，多目标多摄像机跟踪包括三个步骤，即检测，特征提取和数据关联。它还涉及许多边际后处理程序，例如修剪和插值。这项任务是一个复杂而具有挑战性的问题。在这项工作中，我们主要关注数据关联的过程。当在数据关联中采用基于相关性聚类的算法时，可能会观察到严重的信息丢失，尤其是当视频中的行人遇到遮挡时。因此，我们提出了一种称为特征组的方法，该方法可以缓解遮挡下精度的下降。所提出的方法是直觉的，但是在不改变原始框架的情况下易于实现。经过全面的实验，该方法被证明是有效的，并且能够对DukeMTMC数据集进行实质性的改进。相对于其他最新方法，特征组方法也具有竞争力。