Abstract: WildGuard is an intelligent, real-time surveillance system designed to protect wildlife and forest ecosystems. It integrates state-of-the-art computer vision using YOLOv8 and audio classification based on MFCC features and machine learning classifiers to detect humans, vehicles, animals, and gunshot sounds from live camera and microphone feeds. The system automates threat detection, provides instant email alerts, and stores event evidence in a centralized database with a web-based dashboard for monitoring and analysis. This paper presents the system objectives, architecture, modules, algorithms, implementation details, and experimental outcomes.
Keywords: Wildlife Monitoring, YOLOv8, MFCC, Audio Classification, Real-Time Detection, Flask
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DOI:
10.17148/IJARCCE.2026.15127
[1] Mahalakshmi C V, Catherine Ananya M, Supriya L J, Supritha Jogin, Sushmitha S, "WildGuard: A Smart Guardian for Wildlife Using YOLOv8 and Audio Classification," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.15127
