A Multi-View Anomaly Detection Framework for Elephant Movement Based on GPS Data

Abstract

Monitoring elephant movement is crucial for wildlife conservation, especially under threats such as poaching and habitat loss. With the availability of large-scale GPS tracking data, anomaly detection can help identify abnormal behaviors linked to critical events. However, challenges such as data imbalance, GPS noise, and real-time deployment constraints remain. This paper proposes an end-to-end framework for anomaly detection in elephant movement using GPS data. The approach combines multi-view anomaly modeling with a weighted scoring mechanism and a lightweight Random Forest model. To address class imbalance, the pipeline integrates SMOTE (Synthetic Minority Over-sampling Technique), under sampling, and class-weighted learning. Feature selection and quantization further optimize the system for edge and FPGA deployment. Experimental results show strong performance, with F1-Macro ≈ 0.98, ROC-AUC ≈ 0.99, and high recall for anomaly detection. The proposed framework provides an efficient and practical solution for real-time wildlife monitoring.