Intensive pig farming presents substantial challenges for individual health monitoring due to high stocking densities, complex occlusion scenarios, and the need for continuous real-time surveillance. Existing monitoring approaches rely heavily on manual inspection, which is labor-intensive and prone to delayed detection of abnormal behaviors and disease symptoms. This study proposes an embedded intelligent monitoring system integrating a pan-tilt gimbal platform with an improved multi-object tracking and anomaly detection framework for automated pig health surveillance. The system employs a modified PeriodfillDeepSORT algorithm that incorporates a ReID network with appearance features and motion prediction trajectories to maintain identity consistency under occlusion and re-entry scenarios. For anomaly detection, a lightweight YOLOv8-based network was trained on 772 abnormal samples across three behavioral categories: movement abnormalities, postural abnormalities, and disease-related abnormalities. Experimental results demonstrate that the PeriodfillDeepSORT algorithm achieves a Multiple Object Tracking Accuracy (MOTA) of 95. 34%, a Multiple Object Tracking Precision (MOTP) of 94. 77%, and an IDF1 score of 96. 88%, with only 12 identity switches across 2000 frames involving 12 targets—27 fewer than the standard DeepSORT algorithm. In occlusion scenarios, MOTA improved from 61. 1% to 78. 3%. The anomaly detection network achieves an overall detection accuracy of 94. 5%, representing an 8. 8 percentage point improvement over the baseline model, with recognition accuracies of 96. 2% for movement abnormalities, 94. 1% for postural abnormalities, and 92. 8% for disease-related abnormalities. The system operates at 90 frames per second on embedded hardware with a power consumption of 3. 2 watts and a startup time of approximately 1 s, with gimbal angle errors maintained within 3°. These results demonstrate the system’s effectiveness and practical feasibility for real-time intelligent health monitoring in intensive livestock farming environments.
Wang et al. (Thu,) studied this question.