Abstract: Ensuring home safety through effective pet monitoring is essential for pet owners, particularly in preventing potential risks associated with unattended animals. This paper presents a real-time cat detection and surveillance system designed to enhance home security and pet management. The system employs advanced object detection techniques, integrated with automated notification and alert mechanisms, to ensure timely responses. It continuously captures video feeds and images, which are pre-processed using image enhancement techniques to improve detection accuracy. An advanced object detection model analyzes these images in real-time, identifying and classifying cats based on distinctive features such as shape, texture, and movement patterns. To optimize performance, techniques such as dimensionality reduction and feature extraction are applied, reducing computational complexity while maintaining precision. Upon detecting a cat in restricted areas, the system activates automated responses, including real-time notifications to users with details such as the time of detection and location. Additionally, an auditory alert can be triggered to redirect the pet away from unsafe zones. Remote access and control functionalities allow users to manage the system from a distance, ensuring flexible and effective monitoring. The detection model is trained on a diverse dataset of labeled cat images, ensuring high classification accuracy across various lighting conditions and environments. Regular updates and retraining further improve adaptability and performance. By providing reliable, real-time cat detection and alert capabilities, this system enhances home safety, reduces potential hazards, and assists pet owners in maintaining secure and controlled indoor environments. This approach minimizes manual supervision efforts while fostering a safer coexistence between pets and household members.
Keywords: Real-time cat monitoring, home safety, object detection, pet surveillance, automated alerts, remote monitoring, machine learning
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