Optimizing CCTV Storage with Similarity-Detection Algorithms and Cloud Integration

Closed Circuit Television (CCTV) systems are indispensable for surveillance, public safety, and operational monitoring in diverse domains, including transportation, urban management, and medical diagnostics. With the increasing adoption of high-definition video formats like 4K and beyond, the storage burden has grown exponentially. A single CCTV camera can generate tens of gigabytes of data daily, creating substantial storage and management challenges. Traditional compression techniques often degrade video quality, while cloud solutions demand costly infrastructure and high-speed connectivity.This paper introduces a robust framework for optimizing CCTV storage by employing advanced similarity-detection algorithms to eliminate redundant video frames. Algorithms such as Euclidean Distance, Manhattan Distance, Normal Distance, and Cosine Similarity are analyzed and implemented for frame comparison. The framework integrates these methods into a web-based interface that allows seamless storage on Firebase cloud systems, ensuring scalability and accessibility.Through rigorous testing and performance evaluation, the Euclidean Distance method was found to be the most effective, reducing storage requirements by up to 76.81% while preserving video quality. This framework demonstrates immense potential for real-time integration, supporting applications in surveillance, forensic analysis, and traffic monitoring. Future work will expand the framework’s utility by incorporating advanced machine learning techniques and enhanced cloud integration to handle higher-density environments effectively.