IEEE Trans Pattern Anal Mach Intell. 2017 Mar;39(3):589-602. doi: 10.1109/TPAMI.2016.2551245. Epub 2016 Apr 6.
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.
在本文中，我们提出了一种新的方法，该方法基于在线目标特定度量学习和连贯动力学估计，通过网络流量优化进行长时间多人体跟踪中的轨迹段（轨迹片段）关联。我们提出的框架旨在利用外观和运动线索来防止跟踪过程中的身份切换，并恢复漏检。此外，还提出了目标特定的度量（外观线索）和运动动力学（运动线索），以便在线学习和估计，即在跟踪过程中。即使由于遮挡或物体间相互作用导致这些线索无法识别或跟踪目标，我们的方法也非常有效。我们还建议学习这两个跟踪线索的权重，以有效处理严重遮挡和物体间相互作用等困难情况。我们的方法已经在几个公共数据集上进行了验证，实验结果表明它优于几种最先进的跟踪方法。
