Pig aggression classification using CNN, Transformers and Recurrent Networks

The recognition of behavioral relationships related to aggression in pigs is an important task that is visually observed in the livestock industry. However, this task is laborious and susceptible to errors, which can be reduced through automation by visually classifying videos captured in a controlled environment. Video classification can be automated using techniques from computer vision and artificial intelligence, employing neural network methods. The primary techniques utilized in this study are variants of transformers: STAM, TimeSformer, and ViViT, as well as techniques using convolutions, such as ResNet3D2, ResNet(2+1)D, and CnnLstm. These techniques were compared to analyze their individual contributions. The performance was evaluated using metrics such as accuracy, precision, and recall. The TimeSformer technique demonstrated the most promising results in video classification, achieving a median accuracy of 0.729.