Summary of Universal Bovine Identification Via Depth Data and Deep Metric Learning, by Asheesh Sharma et al.
Universal Bovine Identification via Depth Data and Deep Metric Learning
by Asheesh Sharma, Lucy Randewich, William Andrew, Sion Hannuna, Neill Campbell, Siobhan Mullan, Andrew W. Dowsey, Melvyn Smith, Mark Hansen, Tilo Burghardt
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel deep learning system for accurately identifying individual cattle using depth-only data from an off-the-shelf 3D camera. The method relies on CNN and MLP backbones that learn well-generalised embedding spaces from body shape to differentiate individuals, eliminating the need for species-specific coat patterns or close-up muzzle prints. The paper introduces a deep-metric learning method and evaluates two backbone architectures: ResNet and PointNet. The authors also present CowDepth2023, a new dataset containing 21,490 synchronised colour-depth image pairs of 99 cows to evaluate the backbones. Both architectures led to high accuracy that is on par with the coat pattern-based backbone. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps farmers identify individual cattle using cameras and computers. Right now, it’s hard for farmers to track many cattle by hand, so a computer system can help. This system uses special cameras and deep learning algorithms to recognize individual cows based on their body shape. It doesn’t need pictures of the cow’s face or coat pattern, making it useful for farms with many different breeds. The authors tested two versions of this system and found they both worked well. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Resnet
