在线自适应隐马尔可夫模型的多跟踪器融合
Online Adaptive Hidden Markov Model for Multi-Tracker Fusion
摘要：
在本文中，我们提出了一种新的方法，称为HMMTxD的视觉对象跟踪。该方法通过利用隐马尔可夫模型融合来自互补开箱即用跟踪器和检测器的观测，该隐马尔可夫模型的潜在状态对应于表示单个跟踪器故障的二进制向量。马尔可夫模型以无监督的方式进行训练，依赖于在线学习的检测器来为修改的Baum-Welch算法提供与跟踪器无关的信息源，该修改的Baum-Welch算法更新模型w.r.t.部分注释的数据。我们证明了所提出的方法的有效性结合两个和三个跟踪算法。在两个标准基准（CVPR 2013和VOT）和77个公开序列的丰富集合上评估了HMMTxD的性能。HMMTxD在几乎所有标准的所有数据集上都优于最先进的技术，通常是显着的。
英文摘要：
In this paper, we propose a novel method for visual object tracking called HMMTxD. The method fuses observations from complementary out-of-the box trackers and a detector by utilizing a hidden Markov model whose latent states correspond to a binary vector expressing the failure of individual trackers. The Markov model is trained in an unsupervised way, relying on an online learned detector to provide a source of tracker-independent information for a modified Baum- Welch algorithm that updates the model w.r.t. the partially annotated data. We show the effectiveness of the proposed method on combination of two and three tracking algorithms. The performance of HMMTxD is evaluated on two standard benchmarks (CVPR2013 and VOT) and on a rich collection of 77 publicly available sequences. The HMMTxD outperforms the state-of-the-art, often significantly, on all datasets in almost all criteria.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
