IEEE Trans Pattern Anal Mach Intell. 2018 Mar;40(3):595-610. doi: 10.1109/TPAMI.2017.2691769. Epub 2017 Apr 6.
Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.
在线多目标跟踪旨在根据每一帧输入和截至当前时刻提供的信息，即时估计多个目标的轨迹。在复杂场景中，由于在连续帧中关联多个目标的歧义性大，以及目标外观之间的可区分性低，因此仍然是一个难题。在本文中，我们提出了一种鲁棒的在线多目标跟踪方法，可以有效地处理这些困难。我们首先使用轨迹的可检测性和连续性来定义轨迹置信度，并根据轨迹置信度将多目标跟踪问题分解为小的子问题。然后，我们根据置信度值以不同的方式关联轨迹和检测，从而解决在线多目标跟踪问题。基于这种策略，轨迹依次使用在线提供的检测进行扩展，并且无需任何迭代和昂贵的关联步骤就可以将碎片化的轨迹与其他轨迹连接起来。为了在轨迹和检测之间进行更可靠的关联，我们还提出了一种深度外观学习方法，从大型训练数据集中学习有区分性的外观模型，因为传统的外观学习方法不能提供丰富的表示，无法区分具有较大外观变化的多个目标。此外，我们结合在线迁移学习，通过在在线跟踪期间自适应预训练的深度模型来提高外观的可区分性。在具有挑战性的公共数据集上的实验表明，与其他批处理和在线跟踪方法相比，我们的方法具有明显的性能提升，证明了所提出的方法对于在线多目标跟踪的有效性和实用性。
