Last month, I wrote about big data analytics and its ramifications for security from the perspectives of information security and crime event correlation. This month, let’s examine this topic in light of its potential impact on video surveillance.
To recap, Big Data is a name which has been given to masses of data that have been, or are continuously being assembled from a variety of internal and external information sources, producing data that is structured, unstructured or random. While this data has heretofore defied conventional analysis, now, companies such as IBM and RSA (division of EMC) are developing means to derive underlying value from it by surfacing trends, patterns and apparent correlations.
Video presents special challenges. At a recent conference, I heard a 2011 statistic that 220 million frames of video are recorded every second. Assuming an average recording rate of 10 fps, that’s 22 million cameras feeding storage media, which does not seem unreasonable. Given that IP CCTV cameras are outselling analog cameras, and that network bandwidth and storage capacity are increasing while costs are decreasing, it is safe to say that the amount of video stored per month will continue to increase — although it is not known how much of that will be archived beyond 30 days.
This point is significant — if video is not kept for more than 30 days, there is a finite time window for analysis; thus, if big data analytics for video becomes a game-changer, that in itself may argue for longer retention periods.
Finding the Benefits
So where is the opportunity? I believe the answer is in the correlation of captured images and sequences with similar material or with other data that is accessible by the system. We have an example of that (though not big data) in the retail market, where video is correlated with point of sale transactions. In this simple example, a transaction “tag” is placed on the video, enabling an instant view of what transpired at time of transaction. Other types of tags are time/date, alarm inputs and video analytic-related.
Scaling up to use “big data” to find events or to evolve relationships that are not obvious involves several requirements. First, video will need to be marked at the edge, or at least before it hits the storage device. Marking at the edge can be based on the original video data, derived before compression or frame rate or resolution reduction. This marking would include metadata, data or characteristics associated with the video image, which is then transmitted and retained along with the video. IP camera and video analytic technology is just at the threshold of this type of capability.
The more metadata that can be generated, the better the chances that some correlation can be found by a product mining that data, which would involve the application of multiple analytic algorithms on a single frame of video. That could include pre-established rules or a behavior-based algorithm, such as that from BRS Labs, which learns how to spot abnormalities in a scene (Request more info at www.securityinfowatch.com/10482504).
Enhancing Current Applications
Further big data mining opportunities exist in the use of facial recognition. In addition to the facial image capture, metadata about that face is stored and used as the basis for further comparison. Add to this data information about clothing color, height, body shape, etc., and you have a potential people classifier that can be used for further analysis and correlation. Some would argue that that is possible now, but big data could dwarf the scale of today’s limited systems.
Edge marking can mean within the camera, but the intelligence may also be contained in an intermediate processor or within network equipment. An example is Cisco’s Unified Computing System (UCS), which combines network transmission with high density computing and is capable of hosting analytic applications that can be applied to incoming video streams.