Data Archiving Model on Cloud for Video Surveillance Systems with Integrity Check

Video data has grown significantly as a result of the expanding usage of high-resolution cameras and longer retention periods, necessitating effective archiving solutions, such as video surveillance systems, that make data easy to retrieve and maintain data integrity. These systems are relied upon by businesses and private users to protect their assets and guarantee that video recordings are properly archived for later use. In order to solve the issues of data storage, integrity, and retrieval in surveillance systems, this study proposes a data archiving model with an integrity check for video surveillance systems saved on the cloud. The suggested model has four parts: system architecture, data archiving method, data integrity check, and data schema. These components enable metadata production, effective cloud storage, AI-based human identification, and data security. The model’s critical elements are developed, tested, and evaluated in this study to guarantee its dependability and efficiency when managing video surveillance data. Coding the archiving module, testing the software with a pertinent dataset, and creating the integrity check module are all included in the scope. The integrity check module’s performance was tested by comparing compromised and uncompromised data to see how well the model could distinguish between real data and data that had been tampered with. The findings imply that the suggested methodology successfully addresses the problems associated with archiving video surveillance data, improving security, resource management, and data protection. Additionally, it promotes the creation of reliable, tamper-resistant surveillance systems, creating a more dependable digital video surveillance landscape.