Complexity Engineering Society

Complexity of Streaming Video

Quantitative Complexity Management (QCM) technology has been applied to a very wide variety of data types and sources. In this short blog we illustrate the application of QCM to real-time processing of streaming vido. This is how it works:

  • Video is split into frames. The frequency of this depends on the nature of the application (military, surveillance, medical, satellite imagery, traffic, etc.).
  • Each frame is processed so as to prepare it for QCM. Basically this is equivalent to transforming an image into a n x m pixel map in ‘csv’ format (coded by LPG).
  • QCM processes the pixel map and complexity measures at step i are saved.
  • Sudden complexity variations may be used to identify the emergence of anomalies or anomalous features.
  • The process is repeated as necessary.

As small example is illustrated below. Watch video in Youtube.

The 16-second video is split into over 150 frames, separated by 100 msec. The above procedure produces the following complexity evolution over time.

It is interesting to note that, apart from the leftmost peak, which happens because of the large white disk, the rightmost image is not the most complex, as illustrated below.

This is because complexity is structured information and the image in question is so chaotic as to transmit very little information. In fact, the peak in complexity is attained approximately half-way through the video. This may seem counterintuitive, but then, complexity works in mysterious ways!

Established originally in 2005 in the USA, Ontonix is a technology company headquartered in Como, Italy. The unusual technology and solutions developed by Ontonix focus on countering what most threatens safety, advanced products, critical infrastructures, or IT network security - the rapid growth of complexity. In 2007 the company received recognition by being selected as Gartner's Cool Vendor. What makes Ontonix different from all those companies and research centers who claim to manage complexity is that we have a complexity metric. This means that we MEASURE complexity. We detect anomalies in complex defense systems without using Machine Learning for one very good reason: our clients don’t have the luxury of multiple examples of failures necessary to teach software to recognize them. We identify anomalies without having seen them before. Sometimes, you must get it right the first and only time!

1 comment on “Complexity of Streaming Video

  1. Pingback: Image Complexity as Proxy of Traffic Density – Artificial Intuition

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