This paper introduces SFSORT, the world's fastest multi-object tracking
system based on experiments conducted on MOT Challenge datasets. To achieve an
accurate and computationally efficient tracker, this paper employs a
tracking-by-detection method, following the online real-time tracking approach
established in prior literature. By introducing a novel cost function called
the Bounding Box Similarity Index, this work eliminates the Kalman Filter,
leading to reduced computational requirements. Additionally, this paper
demonstrates the impact of scene features on enhancing object-track association
and improving track post-processing. Using a 2.2 GHz Intel Xeon CPU, the
proposed method achieves an HOTA of 61.7\% with a processing speed of 2242 Hz
on the MOT17 dataset and an HOTA of 60.9\% with a processing speed of 304 Hz on
the MOT20 dataset. The tracker's source code, fine-tuned object detection
model, and tutorials are available at
https://github.com/gitmehrdad/SFSORT.

该论文介绍了 SFSORT，这是基于 MOT Challenge 数据集上的实验而开发的世界上速度最快的多目标跟踪系统。通过引入一种新的成本函数，称为边界框相似性指数，该系统实现了准确且计算效率高的跟踪器，消除了卡尔曼滤波器，从而减少了计算需求，并展示了场景特征对增强物体跟踪关联和改进跟踪后处理的影响。使用 2.2 GHz Intel Xeon CPU，在 MOT17 数据集上，该方法实现了 61.7% 的 HOTA 和 2242 Hz 的处理速度，在 MOT20 数据集上，实现了 60.9% 的 HOTA 和 304 Hz 的处理速度。该跟踪器的源代码、优化的物体检测模型和教程可以在 https://github.com/gitmehrdad/SFSORT 上获取。