In recent years, anchor-free object detection models combined with matching algorithms are used to achieve real-time muti-object tracking and also ensure high tracking accuracy. However, there are still great challenges in multi-object tracking. For example, when most part of a target is occluded or the target just disappears from images temporarily, it often leads to tracking interruptions for most of the existing tracking algorithms. Therefore, this study offers a bi-directional matching algorithm for multi-object tracking that makes advantage of bi-directional motion prediction information to improve occlusion handling. A stranded area is used in the matching algorithm to temporarily store the objects that fail to be tracked. When objects recover from occlusions, our method will first try to match them with objects in the stranded area to avoid erroneously generating new identities, thus forming a more continuous trajectory. Experiments show that our approach can improve the multi-object tracking performance in the presence of occlusions. In addition, this study provides an attentional up-sampling module that not only assures tracking accuracy but also accelerates training speed. In the MOT17 challenge, the proposed algorithm achieves 63.4% MOTA, 55.3% IDF1, and 20.1 FPS tracking speed.

翻译：近年来，无锚框目标检测模型结合匹配算法被用于实现实时多目标跟踪，并确保高跟踪精度。然而，多目标跟踪仍面临巨大挑战。例如，当目标大部分被遮挡或目标暂时从图像中消失时，现有的大多数跟踪算法往往会导致跟踪中断。因此，本研究提出一种用于多目标跟踪的双向匹配算法，该算法利用双向运动预测信息来改善遮挡处理。匹配算法中使用一个滞留区域，用于暂时存储跟踪失败的目标。当目标从遮挡中恢复时，我们的方法将首先尝试将其与滞留区域中的目标进行匹配，以避免错误地生成新身份，从而形成更连续的轨迹。实验表明，我们的方法能够改善存在遮挡情况下的多目标跟踪性能。此外，本研究提供了一个注意力上采样模块，该模块不仅能保证跟踪精度，还能加快训练速度。在MOT17挑战中，所提出的算法实现了63.4%的MOTA、55.3%的IDF1以及20.1 FPS的跟踪速度。