SY-Track: A tracking tool for measuring chicken flock activity level

Poultry farming plays a vital role in providing animal protein for human consumption. Implementing efficient automation in chicken farming while ensuring the welfare and health of the birds is crucial. Monitoring changes in chicken activity can reflect their welfare and overall health, and provide a basis for agricultural production systems. Thus, maintaining appropriate levels of activity is vital for successful poultry farming. This paper proposes a method for detecting the activity levels of chicken flocks based on object detection and tracking. Firstly, a dataset for chicken flock detection and tracking was established. Secondly, an improved algorithm named SY-Track was proposed, leveraging optimized YOLOv7-tiny and improved StrongSort to ensure accurate chicken flock detection, tracking, and calculation of activity indices. In the optimized YOLOv7-tiny algorithm, a new lightweight convolution named Spatial Separable and Ghost Convolution (SAGConv) was proposed, complemented by the incorporation of the Efficient Long-range Spatial Aggregation Network (ELAN-SA). SAGConv effectively reduced both model parameters and computational complexity. The proposed ELAN-SA significantly improved the performance of YOLOv7-tiny. Additionally, the adoption of SIOU improved the convergence of bounding box regression. In the improved StrongSort algorithm, the optimized Kalman filter with predicted width and height was employed to enhance tracking performance. Compared to the original model, the improved YOLOv7-tiny reduced the model size by 48.75 %, increased the FPS by 44.15 %, decreased the computational load by 44.62 %, while achieving an AP50 of 96.30 %. The improved StrongSort algorithm achieved impressive MOTA values of 84.33 %, 79.00 %, and 71.62 % on three videos with different camera angles and flock disturbances. Finally, the SY-Track algorithm was utilized for calculating the Unrest Index to reflect activity level of chicken flock. The lightweighting SY-Track algorithm demonstrates superior competitiveness compared to mainstream detection and tracking algorithms when applied to the chicken flock dataset. It effectively calculates the Unrest Index of the chicken flock, providing a foundation for intelligent agricultural production and automation in chicken farming worldwide.