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UPDATED 07:38 EST / JANUARY 03 2014

Avigilon shells out $32M for video analytics firm

Avigilon shells out $32M for video analytics firm
Avigilon Hits The Toronto Stock Exchange

Accounting for more than 50 percent of all internet traffic, video is among the main drivers behind the rapid growth of unstructured information. But while technologies for processing social media interactions and machine logs have garnered a tremendous amount of hype in the enterprise, video analytics is only now entering the mainstream.

At present, this emerging market largely consists of point solutions such as HP’s recently unveiled Autonomy Qfiniti 10 contact center platform and the TubeMogul marketing service. Now Avigilon, a Vancouver-based a provider of closed-circuit surveillance software, is trying to carve out its own niche with the $32 million acquisition of VideoIQ.

Spun out from GE’s security business in 2006, VideoIQ develops video surveillance solutions that leverage machine learning to provide accurate live detection, event verification and instant notification capabilities. The firm lists big name companies like AT&T and Toyota among its customers, as well as the Department of Homeland Security and the U.S. Air Force. Avigilon is buying VideoIQ’s stash of 23 analytics patents as part of the acquisition, which also earns it the technical knowhow of the company’s 30-strong team. The deal is expected to close later this month.

“VideoIQ’s analytics solution complements Avigilon’s innovative line of products,” noted Alexander Fernandes, the president and CEO of Avigilon. “The acquisition will give us sophisticated, commercially proven analytics technology supported by one of the leading analytics development teams in the industry. VideoIQ is transforming the future of intelligent surveillance and I am pleased to welcome the team to Avigilon.”

Fernandes said that VideoIQ’s technology would be packaged with Avigilon’s surveillance platform to give users the “ability to prevent crime proactively” and sift through data patterns more easily at scale.

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