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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Conditional Random Field (CRF)-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning
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Conditional Random Field (CRF)-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning

机译：条件随机场（CRF）-增强：通过CRF学习促进构建健壮的在线混合增强多对象跟踪器

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摘要

Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT). In particular, online MOT is more demanding due to its diverse applications in time-critical situations. A main issue of realizing online MOT is how to associate noisy object detection results on a new frame with previously being tracked objects. In this work, we propose a multi-object tracker method called CRF-boosting which utilizes a hybrid data association method based on online hybrid boosting facilitated by a conditional random field (CRF) for establishing online MOT. For data association, learned CRF is used to generate reliable low-level tracklets and then these are used as the input of the hybrid boosting. To do so, while existing data association methods based on boosting algorithms have the necessity of training data having ground truth information to improve robustness, CRF-boosting ensures sufficient robustness without such information due to the synergetic cascaded learning procedure. Further, a hierarchical feature association framework is adopted to further improve MOT accuracy. From experimental results on public datasets, we could conclude that the benefit of proposed hybrid approach compared to the other competitive MOT systems is noticeable.
机译：由于最新的对象检测器具有合理的可接受性能，因此逐检测跟踪是视觉多对象跟踪（MOT）的标准策略。特别是，由于在线MOT在时间紧迫的情况下具有多种用途，因此要求更高。实现在线MOT的主要问题是如何将新帧上的嘈杂物体检测结果与先前跟踪的物体相关联。在这项工作中，我们提出了一种称为CRF增强的多对象跟踪器方法，该方法利用基于条件随机字段（CRF）促进的在线混合增强的混合数据关联方法来建立在线MOT。对于数据关联，将学习到的CRF用于生成可靠的低级别Tracklet，然后将其用作混合增强的输入。为此，尽管现有的基于增强算法的数据关联方法必须训练具有地面真实信息的数据以提高鲁棒性，但由于协同级联学习过程，CRF增强可确保足够的鲁棒性而无需此类信息。此外，采用分层特征关联框架以进一步提高MOT准确性。从公共数据集上的实验结果，我们可以得出结论，与其他竞争性MOT系统相比，提出的混合方法的优势是显而易见的。

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