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 \url{https://github.com/gitmehrdad/SFSORT}.

翻译：本文介绍了SFSORT——基于MOT挑战数据集实验的全球最快多目标追踪系统。为实现高精度且计算高效的追踪器，本文采用基于检测的追踪方法，沿袭现有文献中建立的在线实时追踪框架。通过引入一种名为边界框相似性指数的新型代价函数，本文消除了卡尔曼滤波器，从而降低了计算需求。此外，本文还展示了场景特征对增强目标-轨迹关联及改进轨迹后处理的作用。在2.2 GHz英特尔至强CPU上，所提方法在MOT17数据集上实现了61.7%的HOTA指标与2242 Hz的处理速度，在MOT20数据集上达到60.9%的HOTA指标与304 Hz的处理速度。该追踪器的源代码、微调后的目标检测模型及教程可在\url{https://github.com/gitmehrdad/SFSORT}获取。