Mark Cuban Funded Analytics Company Shifts Away From Security
Author: IPVM Team, Published on May 10, 2016Login to read this IPVM report.
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Comments (8)
Luis Carmona
What about patent issues as a challenge? Is there a possibility on the concept may draw a patent challenge? Though I concede these days it seems like it would alomost be a given and maybe not worth mentioning anymore.
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Pat Villerot
That is actually really impressive. Good for them! I wonder how much capital is involved and what % stake Cuban ended up with?
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Undisclosed #1
Cuban may be thinking of a synergy with another of his 'counting' analytics companies, MotionLoft.
It is amazing that Cuban is so quick to do a deal again, considering that MotionLoft's founder went off the deep end and bankrupt the company, forcing Cuban to use his credit card just to keep the company afloat. The founder also defrauded various friends and acquaintances selling them non-existent shares in the company.
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Simon Langsford
Netra appears to be more doing content based image search and retrieval in video than literal cross-camera tracking? It will be interesting to see what they are able to patent in this approach, but you can understand Mark Cuban's interest if they have solved the image search problem for fairly generic open searches with reliable results in real time.
There have been others doing this for narrow applications - one that I've seen first hand that works well is the OmniPerception (now Digital Barriers) Magellan video search engine. They built that to search, recognise and count advertising logos, brands and other image patterns in real time during broadcast video to help marketing folks quantify the benefits of advertising during sports events etc.
[Product note] Thanks also for the mention of Snap Surveillance John. Our approach is rather different. We apply computer vision techniques/ video analytics when first deployed on a large camera network to watch and automatically learn how the cameras relate to each other ... but then apply this in a UI for operators that helps THEM do the tracking of a subject very easily and reliably themselves. We didn't seek to build an analytic for fully automated cross-camera tracking (with the performance limitations / variability inherent in such an approach for large networks and busy sites) - but instead focused on a robust, 'one click', visual tool for the operator, using our learning to only show video streams in a view related to the current camera of interest.
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Undisclosed #1
Perhaps trying to steer clear of the Ovigilon Patent Program?
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