Animal Tracking System Using Image Processing For Intrusion Detection And Tracking In Human Settlements

Author(s)	Mr. Mohammed Asad Khan, Mr. Khalid Siddiqui, Mr. Majid Iliyas, Mr. Mohd Ammar, Dr. Ankita Srivastava
Country	India
Abstract	Conservation of biodiversity, human-animal conflict reduction, and ecological study all depend critically on wildlife tracking and monitoring. Often depending on GPS tagging and manual surveillance, traditional approaches can be expensive, time-consuming, and narrow in scope. Automated animal detection systems have benefited much from recent developments in deep learning and real-time picture processing. This work provides a state-of- the-art object identification algorithm, YOLOv8, upgraded real-time animal tracking system combined with React-based front-end and a Flask-based back-end. The system provides real-time location mapping, accurate species identification and classification, instantaneous alerting systems, Comparatively to past efforts, performance analysis reveals gains in detection accuracy, processing speed, and relay time for alarms. Our contributions include a custom-labeled dataset, enhanced deep-learning model deployment, and a real-time alerting system, therefore making this solution rather beneficial for applications in urban security and animal preservation.
Keywords	Animal Tracking, YOLOv8, Deep Learning, Image Processing, Real-time Detection, Alert System
Field	Computer
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-11

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International Journal For Multidisciplinary Research