Multitarget Tracking Using Hough Forest Random Field
Multitarget Tracking Using Hough Forest Random Field
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DOI：
10.1109/tcsvt.2015.2489438
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发表时间：
2016-11
影响因子：
8.4
通讯作者：
Jun Xiang;N. Sang;Jianhua Hou;Rui Huang;Changxin Gao
中科院分区：
文献类型：
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作者：
Jun Xiang;N. Sang;Jianhua Hou;Rui Huang;Changxin Gao
提出了一种新的基于检测的多目标跟踪方法。在我们的框架中有两个主要步骤：数据关联，以形成全球tracklet协会，其次是轨迹估计，以处理剩余的差距。在第一步中，我们制定tracklet关联作为一个推理问题，在霍夫森林随机场，它结合了霍夫森林和条件随机场，使我们能够在一个统一的模型中建模本地和全球tracklet关系。在第二步中，我们改进了可逆跳马尔可夫链蒙特卡罗粒子滤波方法与显式相互遮挡推理，以填补剩余的差距，从第一步，提高整体跟踪精度。在五个公共数据集上进行了广泛的实验，其性能与最先进的方法相当，如果不是更好的话。
This paper presents a novel tracking-by-detection approach for multitarget tracking. There are two major steps in our framework: data association to form global tracklet association, followed by trajectory estimation to deal with the remaining gaps. In the first step, we formulate tracklet association as an inference problem in a Hough forest random field, which combines Hough forest and conditional random field and allows us to model both local and global tracklet relationships in one unified model. In the second step, we improve the reversible-jump Markov chain Monte Carlo particle filtering method with explicit mutual-occlusion reasoning to fill in the remaining gaps from the first step and increase the overall tracking precision. Extensive experiments have been conducted on five public data sets, and the performance is comparable to that of the state-of-the-art method, if not better.