论文标题
快速在线和关系跟踪
Fast Online and Relational Tracking
论文作者
论文摘要
为了克服多个对象跟踪任务中的挑战，最近的算法将相互作用线索与运动和外观特征一起使用。这些算法使用图形神经网络或变压器来提取相互作用功能，从而导致高计算成本。在本文中，提出了一种基于几何特征的新型相互作用提示，目的是检测遮挡和重新识别计算成本低的丢失目标。此外，在大多数算法中，相机运动被认为可以忽略不计，这是一个强有力的假设，并不总是正确的，并且导致目标转换或目标不匹配。在本文中，提出了一种测量摄像机运动和删除其效果的方法，可有效地降低相机运动对跟踪的影响。在MOT17和MOT20数据集上评估了所提出的算法，并在MOT20上实现了MOT17的最先进性能和可比较的结果。该代码也可以公开使用。
To overcome challenges in multiple object tracking task, recent algorithms use interaction cues alongside motion and appearance features. These algorithms use graph neural networks or transformers to extract interaction features that lead to high computation costs. In this paper, a novel interaction cue based on geometric features is presented aiming to detect occlusion and re-identify lost targets with low computational cost. Moreover, in most algorithms, camera motion is considered negligible, which is a strong assumption that is not always true and leads to ID Switch or mismatching of targets. In this paper, a method for measuring camera motion and removing its effect is presented that efficiently reduces the camera motion effect on tracking. The proposed algorithm is evaluated on MOT17 and MOT20 datasets and it achieves the state-of-the-art performance of MOT17 and comparable results on MOT20. The code is also publicly available.
