A Novel Multi-target Multi-camera Tracking Approach based on Feature Grouping
A Novel Multi-target Multi-camera Tracking Approach based on Feature Grouping
复制标题
一种基于特征分组的多目标多摄像机跟踪新方法
DOI：
10.1016/j.compeleceng.2021.107153
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发表时间：
2021
影响因子：
4.3
通讯作者：
Wang Dong
中科院分区：
文献类型：
--
作者：
Xu Jian;Bo Chunjuan;Wang Dong
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。多摄像机多目标跟踪一般包括检测、特征提取和数据关联三个步骤。它还涉及一些边缘后处理过程，如修剪和插值。这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。当采用基于相关聚类的算法进行数据关联时，可能会观察到严重的信息丢失，特别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情况下的准确性下降。该方法在不改变原有框架的前提下，直观且易于实现。经过综合实验，证明了该方法的有效性，并在DukeMTMC数据集上取得了实质性的改进。特征组方法相对于其他最先进的方法也具有竞争力。
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.