In this paper, we present a novel method based on online target-specific
metric learning and coherent dynamics estimation for tracklet (track fragment)
association by network flow optimization in long-term multi-person tracking.
Our proposed framework aims to exploit appearance and motion cues to prevent
identity switches during tracking and to recover missed detections.
Furthermore, target-specific metrics (appearance cue) and motion dynamics
(motion cue) are proposed to be learned and estimated online, i.e. during the
tracking process. Our approach is effective even when such cues fail to
identify or follow the target due to occlusions or object-to-object
interactions. We also propose to learn the weights of these two tracking cues
to handle the difficult situations, such as severe occlusions and
object-to-object interactions effectively. Our method has been validated on
several public datasets and the experimental results show that it outperforms
several state-of-the-art tracking methods.

本文提出一种基于在线目标特定度量学习和协同动力学估计的方法，通过网络流优化来实现长期多人跟踪中的轨迹相关性关联。该方法利用外观和动态线索，避免跟踪时出现身份混淆，并恢复漏检。在跟踪过程中，学习和在线估计目标特定的度量（外观线索）和动态（运动线索），即使由于遮挡或对象间相互作用而无法识别或跟踪目标时仍有效。文中还提出了学习这两种跟踪线索的权重来有效处理严重遮挡和对象间相互作用等困难情况。该方法在多个公共数据集上验证，实验结果表明优于多种最先进的跟踪方法。