Recording and storing video data is a crucial challenge in various domains, including surveillance, autonomous vehicles, and augmented reality. Traditional video recording techniques often lead to large storage requirements and increased inspection time, hindering real-time analysis and efficient storage utilization. In this paper, we propose a novel Storage-Savvy Frame Recorder (SSFR) using computer vision that addresses these limitations. The SSFR optimizes storage usage and reduced inspection time during post-processing and analysis. A threshold value is used to decide the difference between two frames which is to be recorded, to validate the effectiveness of the proposed system, extensive experiments were conducted. The results demonstrate substantial reductions in storage usage, with an average of 80% reduction compared to conventional video recording techniques. Moreover, inspection time was significantly decreased, ensuring real-time analysis and faster access to critical video segments. The SSFR leveraging computer vision capabilities is a promising solution to optimize video storage and enhance inspection efficiency. Its capacity to intelligently capture and store essential frames not only reduces storage requirements but also streamlines video analysis tasks across a wide range of applications, paving the way for more effective and resource-efficient video data management.