Efficient Storage and Analysis of Videos through Motion-Based Frame Removal

The storage and analysis of CCTV footage pose a significant challenge in modern surveillance systems. This paper presents an innovative and modern solution for efficient storage optimization and analysis through motion-based frame removal techniques. By leveraging the OpenCV library, redundant frames are removed, resulting in substantial storage reduction without losing important motion events. The methodology uses Python's OpenCV library with optional technologies such as Django, SQLite, and Bootstrap, ensuring reliability, scalability, usability, and effective data visualization. Experimental results have demonstrated the efficacy across different camera setups in different environments and their ability to preserve critical information. The approach offers a practical solution for decreasing cost, improving analysis efficiency, and enhancing usability. This research contributes to the field of CCTV surveillance by providing a practical and industry-oriented solution. The findings inspire further improvements in storage optimization and pave the way for more intelligent surveillance systems.