International Journal For Multidisciplinary Research

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AI-driven Wildlife Behavior Monitoring using Computer Vision

Author(s) Pandiselvi R, Jeyaprabhu J, Jerome Immanuel Jebaraj J, Muthupandi L
Country India
Abstract 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.
Keywords AI-powered system, Wildlife behavior, Computer vision, YOLOv8, Animal tracking, Behavior classification, Conservation.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-10-25
Cite This AI-driven Wildlife Behavior Monitoring using Computer Vision - Pandiselvi R, Jeyaprabhu J, Jerome Immanuel Jebaraj J, Muthupandi L - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.29257
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.29257
Short DOI https://doi.org/g8pnm5
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