Intelligent Monitoring of Hydraulic Sluices in Plain Regions Using GMM and Kernelized Correlation Filtering

Real-time monitoring of plain-area hydraulic sluices requires reliable detection and tracking of moving targets including unauthorized personnel, abnormally approaching vessels, and floating debris within the forebay. Conventional visual algorithms suffer from high false alarm rates and tracking failures caused by water ripples, drastic illumination shifts, and target occlusion by sluice piers. To address these issues, this study develops a high-precision intelligent monitoring framework integrating an Improved Gaussian Mixture Model and an anti-occlusion Improved Kernelized Correlation Filter. First, an adaptive update coefficient and optimized matching threshold are embedded into Gaussian Mixture Model to suppress periodic water surface noise unique to sluice scenes. Second, Histogram of Oriented Gradients and grayscale features are fused, while an Average Peak Correlation Energy-based dual-indicator confidence evaluation, together with Kalman motion prediction, is introduced to construct a robust Improved Kernelized Correlation Filter tracker against occlusion-induced drift. Finally, the detection and tracking modules constitute a mutually feedback closed-loop pipeline, combined with electronic fence limits and dwell-time thresholds to realize automatic intrusion identification and early warning. Experimental results show that the proposed method achieves an F1-score of 94.12%, a tracking success rate of 84.32%, a minimum center location error of 10.04 pixels, a detection precision of 94.47%, and a tracking precision of 90.86%. The algorithm runs stably at 112 FPS on ordinary CPUs without GPU acceleration, offering a lightweight and low-cost technical solution for real-time unattended safety monitoring of plain-area hydraulic sluices.