Comparison of Transformers with LSTM for classification of the behavioural time budget in horses based on video data

This study compares the performance of Transformers with LSTM for the classification of the behavioural time budget in horses based on video data. The behavioural time budget of a horse consists of amount of time of the activities such as feeding, resting, lying, and moving, which are important indicators of welfare and can be a basis of pain detection. Video technology offers a non-invasive and continuous monitoring approach for automated detection of horse behaviours. Computer vision and deep learning methods have been used for automated monitoring of animal behaviours, but accurate behaviour recognition remains a challenge. Previous studies have employed Convolutional LSTM models for behaviour classification, and more recently, Transformer-based models have shown superior performance in various tasks. This study proposes a multi-input, multi-output classification methodology to address the challenges of accurately detecting and classifying horse behaviours. The results demonstrate that the multi-input and multi-output Transformer model achieves the best performance in behaviour classification compared with single input and single output strategy. The proposed methodology provides a basis for detecting changes in behaviour time budgets related to pain and discomfort in horses, which can be valuable for monitoring and treating horse health problems. This study compares the performance of Transformers with LSTM for the classification of the behavioural time budget in horses based on video data. The behavioural time budget of a horse consists of amount of time of the activities such as feeding, resting, lying, and moving, which are important indicators of welfare and can be a basis of pain detection. Video technology offers a non-invasive and continuous monitoring approach for automated detection of horse behaviours. Computer vision and deep learning methods have been used for automated monitoring of animal behaviours, but accurate behaviour recognition remains a challenge. Previous studies have employed Convolutional LSTM models for behaviour classification, and more recently, Transformer-based models have shown superior performance in various tasks. This study proposes a multi-input, multi-output classification methodology to address the challenges of accurately detecting and classifying horse behaviours. The results demonstrate that the multi-input and multi-output Transformer model achieves the best performance in behaviour classification compared with single input and single output strategy. The proposed methodology provides a basis for detecting changes in behaviour time budgets related to pain and discomfort in horses, which can be valuable for monitoring and treating horse health problems.

• 2025 Douglas Bomford Paper Award

Veterinärmedizinische Universität Wien
KU Leuven