Anomaly Detection System in Surveillance Videos Using Unmanned Aerial Vehicle Systems
Keywords:
Anomaly detection, small-scale unmanned aerial vehicles, feature extraction, real-time applications, support vector machinesAbstract
This paper introduces a One-Class Support Vector Machine (OC-SVM) anomaly detector tailored for aerial video surveillance using small-scale Unmanned Aerial Vehicles (UAVs) operating at low altitudes. Anomaly detection is crucial in various UAV-based surveillance applications, including vehicle tracking, border control, and dangerous object detection. It enables the identification of areas or objects of interest without prior knowledge, enhancing the surveillance capabilities of UAVs. OC-SVM, known for its lightweight and fast classification, enables the implementation of real-time systems, even on low-computational small-scale UAVs. Textural features are employed to detect both micro and macro structures in the analyzed surface. This capability allows the system to identify small and large anomalies critical in low-altitude aerial surveillance scenarios. Experiments conducted on the UAV Mosaicking and Change Detection (UMCD) dataset demonstrate the effectiveness of the proposed system. Evaluation metrics include accuracy, precision, recall, and F1-score. The model achieves 100% precision, indicating it never misses an anomaly, with a recall trade-off reaching up to 71.23%. The proposed model outperforms classical Hara lick textural features by approximately 20% across all evaluation metrics, further validating its effectiveness. The proposed system is compared against existing methods to assess its efficiency and effectiveness in anomaly recognition. Based on the evaluation results, the paper concludes that the proposed model outperforms prevailing methodologies regarding accuracy and performance.
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