T he Police in Chennai have positioned themselves at the forefront of Smart City Surveillance having implemented iOmniscient’s Face Recognition in a Crowd system in a part of this city – the sixth largest in India.

The system installed in the T. Nagar Market enables the police to recognize people of interest outdoors in a totally uncontrolled environment and to track them as they show uzp on different cameras.

Mr. Senthil Manickavasagam, M.D. of Mars Electronics who implemented the system said, “The main requirement from the electronic surveillance used for public safety was that it must function in a difficult outdoor environment. We faced a big problem with this. The many technologies we tried from Russia, USA and Europe worked well when used in a controlled setting indoors, but in the practical crowded scene of an Indian marketplace, nothing worked. Then we implemented iOmniscient’s system. We did it ourselves with just some remote support from them. Thanks to them, my customers now have something that addresses their problem.”

“This system worked with varying lighting, both in open sunlight and with shadows on the face. The top police officers who have seen the system perform, could not believe their eyes. At the T. Nagar Market, the system helped the City Police report a crime rate of zero during the entire Diwali Festival Season.”

Why would you record my shoes in High Resolution?

I f you wanted to recognize me, you would need to see my face in high resolution. Why would you record my shoes in high resolution? However, this is what all VMS systems do.

With the emergence of megapixel cameras, VMS systems record in the resolution of the camera – which consumes enormous storage and network bandwidth (a 2 megapixel camera requires 20 times the storage and bandwidth as a 1x CIF camera). Alternatively, they record in a lower resolution which then means that money spent on the cameras was wasted as all the detail in the image is lost.

iOmniscient’s Smart VMS™ solves this dilemma. The system automatically selects what is important in the scene and records this in high resolution while recording the rest in low resolution. For recognizing a person, the face would be stored in high resolution; the rest of the image can be recorded in low resolution.

The savings in storage can be enormous – the norm is to achieve a reduction of over 80% in storage. If the analysis is done at the edge there can be a similar saving in network bandwidth requirements.

What is considered important can be selected by the user. For instance, he may want to record every face in a scene in high resolution or he may only want to record the faces of people involved in an incident (e.g. if someone fell down or abandoned a bag). There may be situations when it may make sense to record the whole person or even an entire section of the scene in high resolution. The user can decide.

iOmniscient’s Smart VMS sits on top of any VMS and can make it more efficient by telling it which segments of the video need to be recorded in High or Low resolution.

And the Smart VMS from iOmniscient is free (though you do have to pay for any analytics you decide to use in the system).

So why waste your organization’s resources storing images of my shoes in high resolution. Get yourself a smarter system and save money.

   

iOmniscient prides itself as being one of the pioneers in the video analytics industry. Its technologies are robust having been implemented at thousands of sites. However, the company does not rest on its laurels. It continues to invest heavily in new products and capabilities. In just the last 12 months it lodged 18 new patent applications in a variety of technology areas.

This focus on advancing the technology was recognized last week at the Security Exhibition and Conference 2014 in
Australia, where the company won the award for the Best New Product.
 
 
 
Join us at IFSEC INTERNATIONAL, London to witness iOmniscient’s range of intelligent and comprehensive analytics solutions.
Also see a Live Demo of our Facial Recognition in a Crowd system
 
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