as the majority of the fish fry individuals exhibit highly similar appearances, and the feature distinctions between individual targets are not readily apparent. Consequently, fish tracking algorithms relying primarily on appearance-based features for data association often suffer from low accuracy and poor...
Currently, the mainstream tracking algorithms include DeepSORT [15], ByteTrack [16], and StrongSORT [17]. Wu et al. [18] utilized YOLOv5 and DeepSORT algorithms to achieve dynamic identification and automatic counting of fish species in response to significant deformations caused by fish body ...
The third approach is the two-stage deep learning algorithm following the tracking-by-detection paradigm. It generally detects objects by first using a neural network and then associating objects using a filtering method or a reidentification algorithm. Detection algorithms include YOLOx [26], Faster...
We have initiated preliminary experiments by employing YOLOv8 in conjunction with the ByteTrack object-tracking algorithm to track both aggregated and relatively stationary states, as well as normal swimming states. The results are shown in Figure S5. It could be observed that the consecutive frames...
We have initiated preliminary experiments by employing YOLOv8 in conjunction with the ByteTrack object-tracking algorithm to track both aggregated and relatively stationary states, as well as normal swimming states. The results are shown in Figure S5. It could be observed that the consecutive frames...