RESEARCH ON MULTI-OBJECT TRACKING OF PIGS BASED ON IMPROVED BYTE TRACK

As the pig farming industry evolves towards intelligence and large-scale operations, multi-object tracking technology plays a pivotal role in enhancing farming efficiency and optimizing health management. Addressing the issues of trajectory interruption and jitter caused by frequent occlusion of targets, significant scale variations, rapid motion, and sudden turns in large-scale breeding scenarios, an improved ByteTrack method is presented. This method employs YOLOv8 as the detector and enhanced the ByteTrack framework. By incorporating an Exponential Moving Average (EMA) trajectory smoothing module, it optimizes the trajectory jitter issues encountered by traditional Kalman filtering in fast-moving or sudden turning scenarios, resulting in smoother and more continuous tracking trajectories. Experimental results on a real-world livestock farm video dataset demonstrate that the proposed method achieves improvements across several key evaluation metrics. Specifically, the Multi-Object Tracking Accuracy (MOTA) increased by 1.61%, Identification F1 Score (IDF1) improved by 2.55%, and the Recall rose by 9.68%. The results demonstrate that the proposed method can effectively track pigs in complex farming environments with frequent occlusions, large scale variations, and abrupt motion, providing reliable underlying data support for downstream applications, such as abnormal behavior identification and automated physical activity statistics in intelligent pig farming systems.