The most common asked question I receive in regards to DVR's is "How can I optimise storage?"

Therefore, I will discuss 7 commonly available storage optimisation functions available on most NVR/DVR systems. Though not every system has all of these features, all systems offer a number of them, providing strong storage optimisation, and they are as follows;

Video Motion Detection
Advanced Video Analytics
Motion Exclusion Zones
Data Aging
Recording Schedules
CODEC Selection
Dual Streaming

Video Motion Detection (VMD)

Most video surveillance systems use basic motion detection to control recording. Reason being, most applications have significant periods of low activity e.g. nights, weekends and areas of low activity e.g. hallways, motion detection can reduce storage consumption by 50% to 80%. Most systems set their basic motion detection to be fairly conservative so that they rarely miss real incidents. As such, basic motion detection is trusted and used by many types of applications from convenience stores to prisons. Of course, some facilities do not want to take any risk and require continuous recording.

A nice balance that is sometimes achieved is a combination of continuous and motion based recording with a baseline level of continuous recording e.g. 3 frames per second and motion based recording set higher at say 15 fps. This ensures that video is always recorded but storage use is optimised for when activity of interest is most likely to occur, that is, when motion is detected.

Advanced Video Analytics

In more recent time, video analytics has become more accurate at detecting people, faces and vehicles, this intelligence can be used to control recording. I believe this will become one of the most powerful new areas of storage optimisation in the next coming years. Long term storage can be optimised by selectively recording objects most likely to be of long term interest i.e. people, faces and vehicles. Traditionally, long term storage optimisation techniques reduce the quality or the frame rate of all video. With video analytics, storage optimisation techniques can become smarter, increasing the probability of possessing quality long term evidence while minimising total storage consumed.

For instance, in addition to recording video, some systems can record all faces seen on cameras. For example, faces of all the people (100,000+) conducting transactions at a bank branch can be stored at 4CIF quality with less than 20GB of storage. This is 1/100th the amount of storage needed for video and the most important evidence for retail bank's security needs. Of course, today this is just faces but the same process can and will certainly eventually be used to store all the people seen, all the cars moving through an area, etc.

Video analytic companies specialising in perimeter violation are reducing storage needs for those cameras by 90% or more. By placing intelligence in the camera, the camera can only stream or the management system can only record specific objects of interest. For cameras whose main purpose is real time alerting, this is a great storage win. Of course, many cameras are needed for investigation purposes and need storage. As such, this is simply another tool in our collection.

Motion Exclusion Zones

Using basic motion analytics to control recording is enhanced through using motion exclusion zones. It is common for cameras to cover areas that are not of interest to users. Examples include highways behind the building, a tree out front, windows, ceiling lights, etc. Taking a few minutes to set up motion exclusion zones can reduce the storage utilisation by up to 50% on certain cameras. After the first week of a new install, a review should be conducted to tune these settings.

Data Aging

Many systems reduce the number of frames in stored video as the video is older. The basic premise is the older the video, the lower the probability that the video is relevant. Rather than simply delete the video, the size of the video is reduced so that some evidence is available just in case but the storage costs are minimised.

For instance, March Networks has a feature called "Intelligent Video Retention." Avigilon has an advanced data aging solution that specialises in optimising storage for multi-megapixel cameras. In higher end video systems, this type of feature is frequently available. It's quite useful because it can easily double storage duration.

Recording with Schedules

Many organisations have greater security risks at different times of the day. Schedules are a common feature to adjust recording parameters to match those different levels of risks. For instance, an organisation may want continuous recording during business hours but is ok with only having motion based recording after hours. Making this adjustment can reduce video storage use by up to 40%.

CODEC Choice

Choosing a video CODEC that provides the most efficient storage utilisation has been a key component of video system designs for years. While technical issues exists, the trend of moving from less efficient to more efficient CODECs is clear e.g. from MJPEG to MPEG-4 to H.264. The key practical issue currently is the use of H.264 for megapixel cameras due to the high system requirements H.264 demands.

With multiple megapixel manufacturers releasing H.264 megapixel cameras, H.264 megapixel cameras looks certain to be a reality, at least for lower resolution MP cameras. Migrating from MJPEG to H.264 can reduce storage use by 50% or more.

Dual Streaming

To maximise CODECs different strengths and weaknesses, multiple video streams can be used. For instance, H.264 may be the best choice for storage optimisation but MJPEG has advantages of live monitoring e.g. lower delay, lower processing power to view. Most cameras support dual streaming. Video surveillance systems can take advantage of this to reduce storage costs while ensuring optimal live video monitoring.