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.

中文翻译：

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基于置信度的数据关联和判别式深度外观学习，实现稳健的在线多目标跟踪


在线多目标跟踪旨在利用每个传入帧以及当前提供的信息立即估计多个对象的轨迹。在复杂场景中，这仍然是一个难题，因为在连续帧中关联多个对象时存在很大的模糊性，并且对象外观之间的区分度较低。在本文中，我们提出了一种鲁棒的在线多目标跟踪方法，可以有效地解决这些困难。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子问题。然后，我们通过根据置信值以不同方式关联轨迹和检测来解决在线多目标跟踪问题。基于这种策略，轨迹随着在线提供的检测而顺序增长，并且碎片化的轨迹可以与其他轨迹链接起来，而无需任何迭代和昂贵的关联步骤。为了在轨迹和检测之间建立更可靠的关联，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判别性的外观模型，因为传统的外观学习方法不提供丰富的表示来区分具有较大外观变化的多个对象。此外，我们结合在线迁移学习，通过在在线跟踪期间调整预训练的深度模型来提高外观辨别力。对具有挑战性的公共数据集的实验表明，与其他最先进的批量和在线跟踪方法相比，性能有明显的提高，并证明了所提出的在线多目标跟踪方法的效果和实用性。