Robust Online Multiobject Tracking With Data Association and Track Management
Robust Online Multiobject Tracking With Data Association and Track Management
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DOI：
10.1109/tip.2014.2320821
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发表时间：
2014-04
影响因子：
10.6
通讯作者：
Seung-Hwan Bae;Kuk-jin Yoon
中科院分区：
文献类型：
--
作者：
Seung-Hwan Bae;Kuk-jin Yoon
本文研究了复杂场景中的多目标跟踪问题。与使用整个序列检测的批量跟踪系统不同，我们提出了一种新的在线多目标跟踪系统，以便使用在线提供的检测来顺序地建立轨迹。为了在频繁遮挡的情况下稳健地跟踪目标，该系统包括三个主要部分：1)视觉跟踪，通过将在线检测与相应的部分遮挡下的航迹相关联，将数据与航迹存在概率相关联；2)航迹管理，用于关联终止航迹，以链接因长期遮挡而碎裂的航迹；3)在线模型学习，为其他两个部分的成功关联生成判别外观模型。使用具有挑战性的公共数据集的实验结果表明，与其他最先进的跟踪系统相比，该系统的性能有明显的提高。此外，对这三个主要部分进行了广泛的性能分析，证明了每个组件对多目标跟踪的效果和有用性。
In this paper, we consider a multiobject tracking problem in complex scenes. Unlike batch tracking systems using detections of the entire sequence, we propose a novel online multiobject tracking system in order to build tracks sequentially using online provided detections. To track objects robustly even under frequent occlusions, the proposed system consists of three main parts: 1) visual tracking with a novel data association with a track existence probability by associating online detections with the corresponding tracks under partial occlusions; 2) track management to associate terminated tracks for linking tracks fragmented by long-term occlusions; and 3) online model learning to generate discriminative appearance models for successful associations in other two parts. Experimental results using challenging public data sets show the obvious performance improvement of the proposed system, compared with other state-of-the-art tracking systems. Furthermore, extensive performance analysis of the three main parts demonstrates effects and usefulness of the each component for multiobject tracking.