Image-Based Animal Behaviour Analysis Using Convolutional Neural Networks

The significance of animals for human society, ecosystems, and biodiversity makes understanding their behaviour and emotional states incredibly necessary in scientific and societal contexts. Nonetheless, the classic assessment of animal behaviour primarily rests on long-term, manual observation and interpretation, which is often slow, subject to biases, hard to scale across any environment. On the other hand, the ongoing cameans of vision, sensing technologies, and artificial intelligence directions an even more objective and automated way of analyzing animal behaviour. This study deals with the recognition of behaviour-based and emotional states in domestic animals utilizing deep learning methodology with a general perspective on dogs and cats using the data set of images and videos. The proposed system implements influential features from Convolutional Neural Network (CNN) and transfer learning based on VGG16 and VGG19 Structures, which automatically extract some key discriminating visual features of facial expressions, postures, and moving activities. To cater to various problems, such as variations of illuminance, general background clutter, and occlusion issues, pre processing plays a crucial functional role in this system. Public datasets as well as instances from custom-data collection imagery taken in unconstrained conditions were employed for the study. Evaluation was carried out on several performance matrices, accuracy, precision, recall, and F1 score in particular. The experimental results were highly impressive in demonstrating that the presented models have been reciprocated an impressive classification accuracy rate of 92.6% with a custom convolutional neural network (CNN), 95.96% with VGG16, and 95.21% with VGG19 surpassing traditional machine-learning methods. This research, in a wider context, highlights its potential applications in animal welfare monitoring, veterinary diagnostics, and intelligent behavioural assessment.