3.2 Object tracking

When comparing the performance of the OT architectures, we found that the three architectures detected and subsequently tracked more than 120 of the 169 individual fish that swam through the rockwall passage (Table 2). Average precision values for all architectures were above 80% with Seq-NMS being the most precise (93%) at detecting and tracking the yellowfin bream. Recall among architectures were very similar at around 73%. The architecture with the highest overall success at detecting and tracking bream was Seq-NMS (F1=84%) (Table 2).
TABLE 2 Confusion matrix for the three object tracking architectures (MOSSE, Seq-NMS and SiamMask) are shown as counts of individual fish, where the true positive means a bream was detected and tracked correctly for ≥ 50% of the time when it appeared on a video frame, otherwise, it was false negative. False positives were misidentified objects (i.e. algae or other fish) that were detected and tracked.