Summary of Systematic Literature Review Of Vision-based Approaches to Outdoor Livestock Monitoring with Lessons From Wildlife Studies, by Stacey D. Scott et al.
Systematic Literature Review of Vision-Based Approaches to Outdoor Livestock Monitoring with Lessons from Wildlife Studies
by Stacey D. Scott, Zayn J. Abbas, Feerass Ellid, Eli-Henry Dykhne, Muhammad Muhaiminul Islam, Weam Ayad, Kristina Kacmorova, Dan Tulpan, Minglun Gong
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 A machine learning approach combining computer vision with recent advances in AI can help achieve 24/7 livestock monitoring, enabling early detection of animal health issues. This paper reviews computer vision methods and challenges in outdoor animal monitoring, focusing on large terrestrial mammals like cattle, horses, and deer. The image processing pipeline is used to discuss current capabilities and open technical challenges at each stage. Notably, there’s a trend towards using deep learning approaches for tasks like detection, counting, and multi-species classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps farmers monitor their animals 24/7 to improve health and welfare. It uses computer vision and AI to help detect problems early on. The research looks at how this can be done outdoors, where many animals are raised. The focus is on big mammals like cows, horses, and deer. The team shares an image processing pipeline to show what’s currently possible and what challenges remain. They also discuss how deep learning is helping with tasks like finding specific animals. |
Keywords
* Artificial intelligence * Classification * Deep learning * Machine learning
