Identifying Black Cow Actions Using Kalman Filter Velocity and Multi-Stage Classification

抄録

This study proposes an advanced camera-based monitoring system for individual black cows in closed farms. By leveraging computer vision and deep learning, the system identifies five key cow actions: eating, drinking, sitting, standing, and walking. A multi-stage approach classifies actions first as static (eating, drinking, sitting, standing) or dynamic (walking) categories based on Kalman Filter velocity information. Further classification distinguishes among four static actions. A Convolutional Neural Network (CNN) refines especially for sitting and standing. On the other hand, cow head regions and specific zone locations help distinguish eating and drinking. The system achieves an overall accuracy of 80% in long data sequences, demonstrating its potential for precision livestock farming.