Animal Guard: CNN-Driven System for Real-Time Animal Detection in Human Habitats

Abstract

This paper presents the development of a real-time animal detection system, designed to identify wild animals using a live webcam feed and Convolutional Neural Networks (CNNs). The system aims to improve wildlife monitoring by providing real-time detection of animals and alerting users through both auditory and message notifications. By leveraging advanced image recognition techniques, the system can accurately detect and classify various animal species, making it a valuable tool for wildlife conservation and human safety. The platform integrates seamlessly with a user-friendly website built using the Django framework, where users can observe the detection process in real time. The website displays which animals have been detected, allowing users to monitor wildlife activity remotely. Upon detecting an animal, the system immediately triggers an alarm and sends a notification to designated users, ensuring prompt awareness of potential wildlife presence. This dual-alert mechanism enhances safety by enabling quick responses to animal sightings, thereby helping to manage human-wildlife conflicts. The core of the detection process is powered by Convolutional Neural Networks, which have been trained to recognize different animal species from the live video feed. These networks offer a robust solution for real-time image processing and classification, delivering high accuracy in identifying wildlife.