A Novel Deep Learning Framework for Real-Time Livestock Behaviour Detection in Surveillance Systems

Livestock behaviour detection is an essential task for farmers and ranchers, as it can help to identify signs of illness, injury, and stress. Early detection of these problems can lead to timely intervention and improved animal welfare. However, traditional methods of livestock behaviour detection, such as manual observation, require a significant amount of time and efforts. Deep learning has the potential to revolutionise livestock behaviour detection by providing a real-time, automated solution. In this paper, we propose a novel deep learning algorithm for real-time livestock behaviour detection in surveillance systems. Our algorithm is trained on a large dataset of livestock images and videos, and it is able to detect a wide range of livestock behaviours, including eating, standing, laying, walking, and rumination. Our approach leverages state-of-the art deep neural networks to process high-resolution video streams captured by surveillance cameras placed within livestock enclosures. We introduce a custom-designed dual-stream convolutional neural network (CNN) architecture, combining spatial stream along with the spatio-temporal stream optimized for the detection and classification of various livestock behaviours. We have evaluated our algorithm on a real-world livestock surveillance dataset, and it has achieved high accuracy in detecting behaviours. Our algorithm is also able to run in real time, making it suitable for use in practical surveillance systems. Our novel deep learning algorithm has the potential to revolutionise livestock behaviour detection by providing a real-time, automated solution. This could help farmers and ranchers to improve animal welfare, increase productivity, and reduce costs.