Abstract
We present a method for 3D cattle tracking and
inter-camera pose transformation using depth information from
monocular depth estimation with deep networks. Camerabased animal monitoring offers a minimally invasive and easily
adaptable solution for tracking and welfare monitoring, relying
solely on commercial RGB camera systems. However, environmental factors and inter-animal occlusion often hinder tracking
efficacy and consistency. To address these challenges, we developed a pipeline to extract 3D point cloud data of individual
cows in a straw-bedded calving yard environment, generating
quasi-3D bounding boxes (x, y, z, height, width, θ), where θ is
the polar angle. We then estimate the camera system extrinsic
parameters by minimising the rotation, translation, and scale
discrepancies between the apparent motion of animals across
different frames of reference. This approach demonstrates a
strong agreement between the 3D centroids of tracked animals
in motion. Our work advances the development of algorithmic
occlusion handling and object handover techniques in multicamera systems, particularly pertinent to the high-occlusion,
low-locomotion scenario of animals within barn environments
inter-camera pose transformation using depth information from
monocular depth estimation with deep networks. Camerabased animal monitoring offers a minimally invasive and easily
adaptable solution for tracking and welfare monitoring, relying
solely on commercial RGB camera systems. However, environmental factors and inter-animal occlusion often hinder tracking
efficacy and consistency. To address these challenges, we developed a pipeline to extract 3D point cloud data of individual
cows in a straw-bedded calving yard environment, generating
quasi-3D bounding boxes (x, y, z, height, width, θ), where θ is
the polar angle. We then estimate the camera system extrinsic
parameters by minimising the rotation, translation, and scale
discrepancies between the apparent motion of animals across
different frames of reference. This approach demonstrates a
strong agreement between the 3D centroids of tracked animals
in motion. Our work advances the development of algorithmic
occlusion handling and object handover techniques in multicamera systems, particularly pertinent to the high-occlusion,
low-locomotion scenario of animals within barn environments
| Original language | English |
|---|---|
| Publication status | Accepted/In press - 2024 |
| Event | CAFE24: IEEE Conference on AgriFood Electronics - Greece Duration: 26 Sept 2024 → 28 Sept 2024 |
Conference
| Conference | CAFE24 |
|---|---|
| Period | 26/09/24 → 28/09/24 |