AI-driven Wildlife Behavior Monitoring using Computer Vision

Abstract- As human-wildlife interaction grows more frequent and wildlife habitats face increasing environmental pressures, monitoring animal behavior has become crucial for conservation efforts and ecological research. This paper presents an AI-driven Wildlife Behavior Monitoring System using computer vision, deep learning, and YOLOv8 to detect, classify, and analyze wildlife activities in real-time. The proposed system accurately identifies species and tracks behaviors such as feeding, movement, resting, and social interactions across diverse habitats. It provides detailed insights through spatial and temporal mapping, revealing patterns like migration routes and seasonal behavioral changes. Advanced anomaly detection flags unusual behaviors, such as distress or potential poaching, triggering alerts for conservationists. The system’s dashboard visualizes live animal detection, historical data, and behavior reports, assisting researchers in studying long-term behavioral trends. Future features include predictive analytics for forecasting wildlife behavior, edge AI for remote monitoring, and acoustic recognition to monitor elusive species. By offering real-time monitoring and data-driven insights, this AI-powered system aims to revolutionize wildlife research and conservation, ensuring proactive protection and sustainable wildlife management.