Machine Vision - Imagination is the only limit to the applications of Video Analytics

Let’s face it: Big Data lies. It is inadequate, inaccurate, and biased. More often than not it is, granted, good enough even for accurate analyses, but very occasionally visual analytics is the only way to collect truly accurate information.

In video analytics a video stream is divided into frames, each of which is analyzed applying methods based on machine learning. From these picture frames it is possible to identify figures, shapes, quality issues, or even something as abstract as moods of the people, and in real-time. Video analytics is also not limited to the wavelengths visible to human eye. A machine can “see” and analyze, for example, heat leaks.

Video analytics alone enables lots of new business opportunities. When we enrich video data with additional data from several different sources, like sensors and satellites, we can implement applications never seen before.

"Before all the tin foil hats go ballistic, it should be emphasized that these solutions don’t compromise people’s privacy in any way."

Analyzing people and moods

Video analytics can be used to identify people’s moods surprisingly accurately. The machine recognizes so called microexpressions, so it can’t be easily bluffed by, for instance, a forced smile. Video analytics can also estimate a person’s age and gender very accurately. This application could be useful, for example, when analyzing the shopper profiles in shopping centers and stores. It is even possible to build smart screens, which tailor the shown offers for the person nearest to the screen. By taking the customers’ mood into account in the analyses we can time the messages right and identify the places where the customers get bored or angry and decide to leave.

Before all the tin foil hats go ballistic, it should be emphasized that these solutions don’t compromise people’s privacy in any way. The solutions fully comply to the GDPR regulations. In the described examples people are not identified. In fact, their faces are not even recorded. Only a number of data points are collected from each frame, which are then discarded. It is completely impossible to re-engineer the original frames from the data points. With enough data points it is, however, possible to conclude people’s likely gender, age, mood etc.


One of the applications of video analytics is security surveillance. In the construction sites, it is possible to monitor if all the workers have the required safety equipment, helmets, for example. Video analytics can also be used to ensure that there are no people in the prohibited areas.

In traffic monitoring video analytics can identify places of high risk, analyze traffic volumes in real time and monitor that wild animals don’t roam to highways.

Video analytics can even identify weapons. This makes it possible to efficiently monitor the crowds and react swiftly, if anybody takes out a gun or a knife. By monitoring the moods of the crowds with video analytics, it is possible to spot the people who behave nervously or aggressively and might be a safety risk.

Quality assurance

Video analytics can be applied to assure the quality of products during the manufacturing process. A machine can see the tiniest defects and fractures, often much more accurately than a human eye. Analyzing the video data and the sensor data of the production line, the quality of the production can often be monitored very accurately. Especially when a high quality is a competitive advantage, video analytics can bring a significant benefit.

Accurate positioning

GPS-positioning is not always accurate and reliable enough. When there’s a need to locate autonomous, fast moving objects extremely accurately and reliably, sensor fusion analytics comes to rescue. Sensor fusion, simply put, means that we aggregate data streams from several different data sources, and analyze this data often in real-time. Data sources include, but are not limited to, sensors, satellites, magnetic positioning devices, and video data of the surroundings. No known positioning method works reliably in all conditions and places alone: GPS signal is lost in tunnels, video analytics fails in a snowstorm, magnetic sensors can easily be interfered with metal objects. When using several data sources and methods, we never get lost even in extreme conditions.

Machine is superior to human in vision too. In video and sensor fusion analytics a machine is accurate, tireless, and does not make mistakes. Or if it makes a mistake, it makes the same mistake systematically, always in the same situation, so the error can be tracked down and corrected. A human, in comparison, makes random mistakes, lets mood and vigor affect her judgement, and gets tired.

What was leading edge yesterday, is routine today. Think about OCR-technology, for instance. Today almost every parking hall reads the register plates automatically, and nobody wonders anymore. Any competitive advantage becomes a requirement fast. So, if you prefer to lead, and not to follow, contact us. 🙂

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Topics: Video Analytics



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