A low‐cost, long‐running, open‐source stereo camera for tracking aquatic species and their behaviours

Abstract

Ecologists are now widely utilising video data to quantify the behaviours and interactions of animals in the wild. This process can be facilitated by collecting videos in stereo, which can provide information about animals' positions, movements and behaviours in three‐dimensions (3D). However, there are no published designs that can collect underwater 3D stereo data at high spatial and temporal resolutions for extended periods (days). Here, we present complete hardware and software solutions for a long‐running, open‐source, underwater stereo camera rig, costing £1337. This stereo camera can continuously record aquatic species and their behaviours/interactions in high resolution (1080 p and 30 fps) and in 3D, over multiple days. We provide full design guides for the cameras and a travel‐friendly rig, and include guidance and open‐source code for calibrating the cameras in space and time. We also show how these cameras could be used to track animals' body parts and positions, and how their size, posture and behaviour can be inferred. This stereo camera will facilitate the collection of high‐resolution ecological and behavioural data, such as affiliative, agonistic or trophic interactions between species, which can inform us about the health and structure of ecosystems. These data will assist ecologists and conservationists in monitoring and understanding the impacts of current environmental pressures on ecosystem functioning.

A Variable Photo-Model Method for Object Pose and Size Estimation with Stereo Vision in a Complex Home Scene

“…Furthermore, while there have been studies exploring the use of infrared (IR) stereo imaging for vegetable classification [ 32 ], the available stereo benchmark datasets primarily consist of RGB imagery and lack object size information. This lack of comprehensive benchmark datasets has led many studies in stereo vision pose estimation to rely on their own target-specific datasets instead of publicly available benchmarks [ 33 , 34 ]. As a result, it is common for researchers in the field of stereo vision pose estimation to utilize their own datasets.…”

Peering into the world of wild passerines with 3D-SOCS: synchronized video capture and posture estimation

“…Although initial applications were primarily in controlled laboratory settings [34][35][36][37][38][39][40][41][42][43][44][45][46], computer vision is also being applied in the field. It has been used to monitor animals from drones [47][48][49], quantify courtship displays [50], analyze parental care behaviors [51], study underwater interactions [52], identify individuals [53,54], and more.…”