Reducing Video Surveillance Storage Costs

The growth of video surveillance data heavily impacts network bandwidth and increases storage demands. Here's a look at how one vendor, Bosch, is working to address these challenges.

David Hill

February 11, 2014

4 Min Read
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IT cannot ignore the impact of the Internet of Things (IOT). As an example, digital video surveillance cameras are more and more being deployed as edge devices on the corporate backbone network. Not only does this impact the bandwidth of the network when data passes from the camera to a central IT location, but the accumulation of data from many different cameras and longer retention periods can quickly expand storage, making the long-predicted data explosion very tangible.

Is your IT organization prepared not only for the added burden of storage costs, but also the management issues that arise as a result, such as proper retention periods and necessary video resolution requirements to achieve business goals?

Let’s consider what’s happening at the edge, and how at least one vendor, Bosch, is working to reduce these burdens on the information infrastructure.

Intelligent Imaging Technology

The networkable digital cameras used in video surveillance are really computers with a lens attached. The camera hardware also includes local storage (for instance, 64 GB) so that the camera does not have to send everything (at least immediately and maybe not ever) to the central IT location. The software not only includes an operating system, but application software whose algorithms perform analytical tasks to manage the camera’s imaging capturing processes. And that makes these cameras “intelligent” rather than the “dumb” ones that basically only take pictures.

Let’s examine image processing using Bosch cameras as an example. Keep in mind that Bosch shares the video surveillance market with other companies, including Axis Communications, Panasonic, and Samsung.

Bosch refers to what it does as intelligent imaging. Its starlight imaging technology produces usable images in low-light scenarios. Lighting conditions in surveillance scenarios are not always optimal, say, on a heavily overcast day or in an industrial building at night with minimal lighting. As a result, the ability to extract a color image of usable quality from a scene of interest while not breaking the bandwidth bank is very important.

The improved sensitivity of Bosch’s latest generation of cameras reduces the bandwidth by about an order of magnitude over its previous generation. Lower bit rates imply not only that less data has to be transmitted when needed, but also that less data has to be stored at a central site. Not only that, but data can be stored locally for longer time periods, which reduces or may even obviate the need to send the data to a central site at all.

Next, Bosch controls image processing with its Content Based Imaging Technology (CBIT), a scene-analysis system that examines the actual content of a video image and provides feedback to adjust image processing. For example, although numerous video surveillance applications have constant motion tracking features, there are a number that offer infrequent motion capture, activating the camera only when movement is detected, such as a person trying to cross a fence.

Bosch’s CBIT detects motion and activates intelligent auto exposure only for the duration of the motion to improve the level of detail captured of the moving object or person.

[Read how IBM is focused on software-defined storage and the importance of storage integration with networking, virtualization, big data, and the cloud in "IBM Emphasizes Integrated Storage."]

Intelligent Dynamic Noise Reduction (iDNR) technology is also a part of CBIT. Noise, in an image sense, is extraneous or unwanted data in an image, i.e., a random pattern of pixels that lessens the clarity of the image. Noise is a normal byproduct of attempting to capture images by raising settings to make the camera more “sensitive” in low-light digital video and photography.

Noise can occur in a spatial sense (as in low light in a frame) or temporal (as in motion among progressive frames). Bosch’s iDNR algorithms apply analytics to dynamically reduce the noise and the company claims that noise reduction can also reduce bandwidth requirements by up to 50% in certain scenes (although your mileage might vary!).

Taken together, Bosch’s digital surveillance technologies appear to substantially reduce storage and network demands on central IT.

Mesabi Musings

Over the years, IT has had to adjust from primarily providing online transaction processing systems to also supporting collaboration technologies (such as email and document sharing) and an increasing number of Web-based services. Now, the Internet of Things is demanding the attention of businesses and their IT organizations. IT organizations had better understand how new technologies work and make sure that it is involved in decisions that affect its cost structure (i.e., storage). Otherwise, they may get some unpleasant surprises.

Networked digital cameras for video surveillance offer tangible evidence of how intelligent devices at the edge can seriously impact the larger information infrastructure. Bosch’s use of intelligent imaging technology is a good example of how video surveillance technologies can provide usable images in a wide variety of conditions, but also reduce the impact of these edge technologies on IT infrastructure performance and costs.

Bosch is not a client of David Hill and the Mesabi Group.

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