Exploiting Long-Term Connectivity and Visual Motion in CRF-Based Multi-Person Tracking
Exploiting Long-Term Connectivity and Visual Motion in CRF-Based Multi-Person Tracking
复制标题
DOI：
10.1109/tip.2014.2324292
复制
发表时间：
2014-05
影响因子：
10.6
通讯作者：
A. Heili;Adolfo López;J. Odobez
中科院分区：
文献类型：
--
作者：
A. Heili;Adolfo López;J. Odobez
我们提出了一个条件随机场的方法来跟踪的检测，在该方法中，我们模型成对的因素连接对的检测和他们的隐藏标签，以及更高的顺序定义的标签成本方面的潜力。与以前的论文相反，我们的方法考虑了检测对之间的长期连接性，并基于位置，颜色和新颖性，视觉运动线索，模型相似性以及它们之间的差异。我们引入了一组特定于特征的置信度分数，其目的是根据其可靠性来加权特征贡献。然后，以无监督的方式从检测或从tracklet学习成对潜在参数。标签成本的定义，以惩罚的复杂性的标签，基于先验知识的场景，如入口/出口区的位置。在PETS'09、TUD、CAVIAR、Parking Lot和Town Center公共数据集上的实验表明了该方法的有效性，其性能接近或优于当前最先进的算法。
We present a conditional random field approach to tracking-by-detection in which we model pairwise factors linking pairs of detections and their hidden labels, as well as higher order potentials defined in terms of label costs. To the contrary of previous papers, our method considers long-term connectivity between pairs of detections and models similarities as well as dissimilarities between them, based on position, color, and as novelty, visual motion cues. We introduce a set of feature-specific confidence scores, which aim at weighting feature contributions according to their reliability. Pairwise potential parameters are then learned in an unsupervised way from detections or from tracklets. Label costs are defined so as to penalize the complexity of the labeling, based on prior knowledge about the scene like the location of entry/exit zones. Experiments on PETS'09, TUD, CAVIAR, Parking Lot, and Town Center public data sets show the validity of our approach, and similar or better performance than recent state-of-the-art algorithms.