Yang Ehwa, Gwak Jeonghwan, Jeon Moongu
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea.
Sensors (Basel). 2017 Mar 17;17(3):617. doi: 10.3390/s17030617.
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.
由于当前最先进的目标检测器具有合理可接受的性能，基于检测的跟踪是视觉多目标跟踪（MOT）的标准策略。特别是，在线MOT因其在时间关键情况下的多样化应用而要求更高。实现在线MOT的一个主要问题是如何将新帧上有噪声的目标检测结果与先前跟踪的目标进行关联。在这项工作中，我们提出了一种称为CRF-boosting的多目标跟踪器方法，该方法利用基于条件随机场（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，学习到的CRF用于生成可靠的低级轨迹段，然后将这些轨迹段用作混合增强的输入。为此，虽然基于增强算法的现有数据关联方法需要具有地面真值信息的训练数据来提高鲁棒性，但由于协同级联学习过程，CRF-boosting在没有此类信息的情况下也能确保足够的鲁棒性。此外，采用分层特征关联框架来进一步提高MOT的准确性。从公共数据集的实验结果来看，我们可以得出结论，与其他有竞争力的MOT系统相比，所提出的混合方法的优势是显著的。
