  We investigate the application of ByteTrack in the realm of multiple object
tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous
tracking of multiple objects by strategically incorporating detections with a
low confidence threshold. Conventionally, objects are initially associated with
high confidence threshold detections. When the association between objects and
detections becomes ambiguous, ByteTrack extends the association to lower
confidence threshold detections. One notable drawback of the existing ByteTrack
approach is its reliance on a fixed threshold to differentiate between high and
low-confidence detections. In response to this limitation, we introduce a novel
and adaptive approach. Our proposed method entails a dynamic adjustment of the
confidence threshold, leveraging insights derived from overall detections.
Through experimentation, we demonstrate the effectiveness of our adaptive
confidence threshold technique while maintaining running time compared to
ByteTrack.
