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bioRxiv · 10.64898/2026.07.22.740183

Tracking animal routes in 3D space through reconstructed habitats from dynamic videos

Daniel, M. M. M.·Santon, M.·Narendra, A.·How, M. J.
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Abstract

Contextualizing the movements of animals into their three-dimensional (3D) habitat contexts is still a major challenge for fields relying on animal tracking methods. This promises to change as Structure-from-Motion photogrammetry tools and techniques revolutionize image processing into the truly 3D spatial realm, by enabling reconstructions of habitat models from overlapping photographs. Combined with tracking data, these techniques would help elucidate drivers behind animal movements that have been previously masked by two-dimensional approaches. Unfortunately, tracking methods are often still impractical for use with understudied or non-model animals, especially those living underwater. In this paper, we describe a method for tracking the translational movements of animals into a photogrammetric habitat model. Our approach spans three general parts: (1) filming the navigation paths of wild animals by following individuals with small cameras (GoPros) on extendable sticks whilst SCUBA diving, (2) reconstructing a 3D spatial habitat model from separate footage, and (3) manually plotting the 3D trajectories of animals into the habitat model. We used one popular commercial software for photogrammetric reconstruction, trajectory plotting, and measurement of trajectories, after which the plots can be exported in a variety of formats for further analyses. Straightforward and flexible methodologies such as this stand to encourage more fieldwork concerning animals that live in structurally complex habitats, or animals that are underrepresented in movement or navigation research. We expect that this approach can be adapted to study many aquatic or terrestrial animals in different habitats, and at various scales.

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Daniel, M. M. M., Santon, M., Narendra, A., How, M. J.. 2026-07-23. Tracking animal routes in 3D space through reconstructed habitats from dynamic videos. https://doi.org/10.64898/2026.07.22.740183

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