Summary of Pig Aggression Classification Using Cnn, Transformers and Recurrent Networks, by Junior Silva Souza et al.
Pig aggression classification using CNN, Transformers and Recurrent Networks
by Junior Silva Souza, Eduardo Bedin, Gabriel Toshio Hirokawa Higa, Newton Loebens, Hemerson Pistori
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed study leverages computer vision and artificial intelligence techniques to analyze animal behavior, particularly aggressiveness in pigs, which can inform decision-making in farm production. By automating the process of classifying videos captured in controlled environments, the researchers aimed to reduce errors and time consumption associated with manual analysis. The study employed variants of transformers (STAM, TimeSformer, ViViT) and convolution-based techniques (ResNet3D2, Resnet(2+1)D, CnnLstm) for video classification, focusing on identifying aggressive and non-aggressive behaviors in pigs. The evaluation metrics used included accuracy, precision, and recall, with the TimeSformer technique demonstrating the best results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a system that uses artificial intelligence to understand animal behavior. They wanted to make it easier and more accurate for farmers to know if their pigs are being aggressive or not. To do this, they used special computer programs to look at videos of pig behavior and identify patterns. The program was tested with different methods and one called TimeSformer worked the best. This could help farmers make better decisions about how to take care of their pigs. |
Keywords
» Artificial intelligence » Classification » Precision » Recall » Resnet
