Reducing Bandwidth and Storage Requirements for Surveillance Videos Using ROI Extraction and Compression

Cloud storage capacity for surveillance videos is restricted by the massive amounts of bandwidth needed to upload them and the substantial storage space they consume. As a result, researchers are actively exploring methods to reduce file sizes while maintaining critical visual details. The goal is to achieve a balance between minimizing the storage size required and preserving video quality, all while keeping costs as low as possible and achieving the best possible results. This paper proposes a developed approach to optimize surveillance video compression. Specifically, each frame, after being acquired, is applied to extract regions of interest (ROI) where motion has occurred, ensuring that the entire desired area is captured. Next, the extracted areas with no changes are zeroed out, reducing unnecessary data. The resulting area is then encoded and transmitted. When the encoded video is received, the decoding process is carried out to restore the video to its original content, i.e., video frames. Three datasets are used to evaluate the performance of the developed algorithm. In addition, the obtained results are compared to the basic video, which shows that the developed algorithm produced good results, outperforming the MJPEG algorithm and existing algorithms.