Real-Time Wildlife Tracking and Anomaly Detection Using YOLOv8

Wildlife monitoring and conservation have become crucial as human activities increasingly encroach on natural habitats. Automated animal detection systems offer valuable support in addressing this challenge. This project aims to develop a real-time animal detection system using a Laptop Processing Unit and curated datasets. The primary goal is to provide a cost-effective and accessible tool for researchers, conservationists, and wildlife enthusiasts to track and study animal populations in their natural habitats. By leveraging images captured from cameras in the wild or controlled settings, the system can identify and categorize various animal species. The project utilizes diverse datasets containing images of both wildlife and domestic animals, which are preprocessed to extract relevant features and labels. These datasets enable the training of machine learning models optimized for the limited computational resources of the Laptop Processing Unit. This research aims to create a system that is both efficient and affordable, with significant applications in wildlife conservation, animal monitoring, and even agricultural management. A key feature of the system is its ability to detect potential security breaches, unauthorized access, and other anomalies in the monitored area. When such a threat is identified, an alert system, such as a buzzer, is activated.