Pig aggression classification using CNN, Transformers and Recurrent
  Networks
Junior Silva Souza Eduardo Bedin Gabriel Toshio Hirokawa Higa Newton Loebens Hemerson Pistori
The development of techniques that can be used to analyze and detect animal
behavior is a crucial activity for the livestock sector, as it is possible to
monitor the stress and animal welfare and contributes to decision making in the
farm. Thus, the development of applications can assist breeders in making
decisions to improve production performance and reduce costs, once the animal
behavior is analyzed by humans and this can lead to susceptible errors and time
consumption. Aggressiveness in pigs is an example of behavior that is studied
to reduce its impact through animal classification and identification. However,
this process is laborious and susceptible to errors, which can be reduced
through automation by visually classifying videos captured in controlled
environment. The captured videos can be used for training and, as a result, for
classification through computer vision and artificial intelligence, employing
neural network techniques. The main 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 employed for pig video classification with the objective of
identifying aggressive and non-aggressive behaviors. In this work, various
techniques were compared to analyze the contribution of using transformers, in
addition to the effectiveness of the convolution technique in video
classification. The performance was evaluated using accuracy, precision, and
recall. The TimerSformer technique showed the best results in video
classification, with median accuracy of 0.729.