猪攻击行为分类：基于CNN、Transformer和循环网络
Pig aggression classification using CNN, Transformers and Recurrent Networks
AI总结：
本研究利用CNN、Transformer和循环网络对猪的攻击行为视频进行分类，比较多种技术，发现TimeSformer表现最佳，中位准确率达0.729。
摘要：
开发可用于分析和检测动物行为的技术对于畜牧业至关重要，因为这能够监测压力和动物福利，并有助于农场的决策制定。因此，开发应用程序可以帮助饲养员做出决策，以提高生产性能并降低成本，因为动物行为通常由人工分析，这容易导致错误且耗时。猪的攻击性是研究的行为之一，旨在通过动物分类和识别来减少其影响。然而，这一过程费力且容易出错，可以通过在受控环境中对拍摄的视频进行视觉分类来实现自动化，从而减少错误。拍摄的视频可用于训练，并进而通过计算机视觉和人工智能（采用神经网络技术）进行分类。本研究使用的主要技术是Transformer的变体：STAM、TimeSformer和ViViT，以及使用卷积的技术，如ResNet3D2、Resnet(2+1)D和CnnLstm。这些技术被用于猪视频分类，旨在识别攻击性和非攻击性行为。在本工作中，比较了多种技术，以分析使用Transformer的贡献，以及卷积技术在视频分类中的有效性。性能通过准确率、精确率和召回率进行评估。TimeSformer技术在视频分类中表现出最佳结果，中位准确率为0.729。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
