The Storage Dilemma
Traditional real-time video surveillance systems can generate a massive amount of data. A single high-definition camera recording at 30 frames per second (fps) can generate gigabytes of data every hour. When multiple cameras are in use for 24/7 surveillance, the storage requirements can quickly become overwhelming. As a result, organizations often face the daunting task of managing an ever-expanding archive of video footage, which can result in high costs for storage infrastructure and maintenance.
The Archive Depth Challenge
Beyond the sheer size of the video files, another problem arises when it comes to archive depth, which refers to the duration for which the footage is stored. In many applications, it may be necessary to keep video records for extended periods, ranging from weeks to months or even years. Storing such a vast amount of data can be prohibitively expensive and complex, not to mention the difficulties in searching through the archives for specific events or time periods.
Solutions and Alternatives
One approach to mitigate storage and archive depth issues is to use video analytics either on the camera itself or on a local computer. Advanced algorithms can analyze the video feed in real-time to detect significant events or activities. If the algorithm detects no movement or activity in the frame, it can automatically reduce the frame rate, thereby saving storage space.
In addition to video analytics, time-lapse surveillance itself offers a natural solution to the problem. By capturing fewer frames per minute or hour, the amount of data generated is significantly reduced. If the scene being monitored is relatively static, with sporadic events of interest, then a time-lapse approach is ideal. This is particularly true for applications like construction site monitoring, where changes occur slowly over a long period.
A hybrid model combining real-time surveillance with time-lapse techniques can also be effective. Cameras can be programmed to switch between real-time and time-lapse modes based on triggers such as motion detection or other analytic insights. This allows for real-time monitoring when needed, while still benefiting from the reduced storage needs of time-lapse video for less critical periods.