  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}.
