NOFTrack: Efficient Multi-Object Tracking with a Speed Advantage

In the field of multi-object tracking (MOT), accurately tracking the position and characteristics of targets is crucial. However, traditional methods often overlook detection results with low confidence, which can lead to the omission of targets and the fragmentation of trajectories. To overcome these challenges, we have introduced a novel NOF algorithm. This algorithm efficiently handles detection boxes through a tiered strategy, initially using an adaptive Kalman filter for preliminary matching, followed by a secondary screening of detection boxes with lower confidence, and a final stage for processing dubious detection results. This approach not only enhances the speed and accuracy of tracking but also performs exceptionally well when dealing with complex scenes. Moreover, it is capable of achieving high efficiency, with a rate of over 300 frames per second (FPS), ensuring the efficiency and precision of the tracking process.