Non-invasive extraction of white shark swimming kinematics from Unoccupied Aircraft System (UAS) imagery

Alexandra DiGiacomo; Ann Marie Abraham; Barbara Block

Just Accepted

This article has been peer reviewed and accepted for publication. It is in production and has not been edited, so may differ from the final published form.

Non-invasive extraction of white shark swimming kinematics from Unoccupied Aircraft System (UAS) imagery

Alexandra DiGiacomo , Ann Marie Abraham, Barbara Block

Abstract

Context: Consumer-grade Unoccupied Aircraft Systems (UAS) are increasingly being used by both scientists and hobbyists in the coastal environment. Marine megafauna are observed via UAS as part of monitoring programs, recreational interests, and scientific research, amassing aerial imagery datasets. As manual documentation of these datasets is infeasible at scale, efficient approaches leveraging computer vision and deep learning have emerged to detect and classify marine megafauna. Aims: This study provides a workflow to quantitatively estimate swimming kinematics tailbeat frequency (TBF) and tailbeat amplitude (TBA) of white sharks (Carcharodon carcharias) from aerial UAS video data. Methods: Body pose estimation was performed using computer vision model DeepLabCut to track six key white shark body parts across UAS videos. The relative positions of these body part coordinates were used to compute tail position over time and quantify TBF and TBA across a population of white shark in Monterey Bay, California. Key Results: With a training set of just 52 images, the deep residual neural network reaches human-level labeling accuracy of body parts (Root Mean Square Error < 1.3 cm). This workflow is applied to 76 focal follows representing 34 individuals to produce TBF (0.43 ± 0.07 Hz) and TBA (0.24 ± 0.10 BL) values similar to those derived from biologging devices previously deployed on individuals in this population. Conclusions: The results indicate that body pose estimation via DeepLabCut can allow for the rapid extraction of quantitative kinematics like TBF and TBA in juvenile white shark populations that aggregate in coastal habitats. Implications: This approach provides a non-invasive, scalable method to understanding megafauna kinematics in sensitive species that overcomes the logistical barriers of traditional biologging approaches.

WR24193 Accepted 28 June 2025