Intelligent Identification of Security Intrusions Around Wind Farms Based on Video Surveillance and Target Tracking

Abstract

This study presents an intelligent framework for identifying security intrusions around wind farms by integrating advanced video surveillance and target tracking technologies. To address the challenges posed by dynamic outdoor environments — such as occlusions, illumination changes, and large scale spatial layouts — the framework introduces two core components: The Dynamic Surveillance Intrusion Detection Model (DSIDM) and the Dynamic Intrusion Detection Framework (DIDF). The DSIDM leverages a hierarchical design combining convolutional neural networks for spatial feature extraction and recurrent structures for modeling temporal dependencies. It incorporates feature encoding, object localization, motion pattern understanding, and multiobject tracking to accurately recognize diverse categories, including authorized personnel, wildlife, and potential intruders. A decision mechanism further integrates spatial temporal cues to evaluate intrusion likelihood with high robustness. Complementing this, the DIDF employs adaptive surveillance principles to refine detection under real-world operational variability. It integrates spatial–temporal prediction, probabilistic modeling, reinforcement learning-based decision optimization, and noise reduction mechanisms to ensure reliable performance under fluctuating weather, camera noise, and complex motion behaviors. By dynamically adjusting thresholds and surveillance actions, the DIDF enhances responsiveness and reduces false alarms. Extensive experiments conducted on multiple wind farm-related surveillance datasets demonstrate the effectiveness of the proposed framework, showing improvements in accuracy, robustness, and computational efficiency compared with state of the art methods. Together, the DSIDM and DIDF provide a scalable and adaptive solution for real time protection of wind farm infrastructure.