Universal Bovine Identification via Depth Data and Deep Metric Learning
Asheesh Sharma Lucy Randewich William Andrew Sion Hannuna Neill Campbell Siobhan Mullan Andrew W. Dowsey Melvyn Smith Mark Hansen Tilo Burghardt
This paper proposes and evaluates, for the first time, a top-down (dorsal
view), depth-only deep learning system for accurately identifying individual
cattle and provides associated code, datasets, and training weights for
immediate reproducibility. An increase in herd size skews the cow-to-human
ratio at the farm and makes the manual monitoring of individuals more
challenging. Therefore, real-time cattle identification is essential for the
farms and a crucial step towards precision livestock farming. Underpinned by
our previous work, this paper introduces a deep-metric learning method for
cattle identification using depth data from an off-the-shelf 3D camera. The
method relies on CNN and MLP backbones that learn well-generalised embedding
spaces from the body shape to differentiate individuals -- requiring neither
species-specific coat patterns nor close-up muzzle prints for operation. The
network embeddings are clustered using a simple algorithm such as $k$-NN for
highly accurate identification, thus eliminating the need to retrain the
network for enrolling new individuals. We evaluate two backbone architectures,
ResNet, as previously used to identify Holstein Friesians using RGB images, and
PointNet, which is specialised to operate on 3D point clouds. We also present
CowDepth2023, a new dataset containing 21,490 synchronised colour-depth image
pairs of 99 cows, to evaluate the backbones. Both ResNet and PointNet
architectures, which consume depth maps and point clouds, respectively, led to
high accuracy that is on par with the coat pattern-based backbone.