October 30, 2019
WELLESLEY, Mass., October 30, 2019– Due to an expanding number of potential applications and improvements to the technology such as faster hardware, computer vision and machine vision will see substantial growth over the forecast period, according to a report by BCC Research, “Computer Vision and Machine Vision in Everyday Life.”
The market expects to see a compound annual growth rate (CAGR) of 11.7% through 2024, when it could be worth $26.0 billion.
“Demand for MV systems has increased in all everyday life applications, including medical devices, packaging, automotive, printing and publishing, consumer goods, traffic management and toll collection,” writes analyst Srinivasa Rajaram. “Applications, such as automatic plate number recognition, traffic flow monitoring, traffic surveillance and other related applications are witnessing increased integration and utilization of MV systems. The components of MV systems and the technologies involved have become more intricate and sophisticated. Higher vision-processing hardware speed has been a key factor to both faster parts-per-minute throughput and greater robustness in manufacturing MV tools. Vision processing is currently performed at substantially faster rates using hardware that requires far less electrical power. Faster hardware, more intelligent tools and better application software development will enable a broader and deeper proliferation of MV in everyday life applications. Customers’ sophisticated demands are additional factors that are having positive effects on the MV industry.”
The Utility of Machine Vision in Security
MV serves security systems to detect unauthorized presence of people and objects, as well as to identify known criminals in various sensitive locations, the report adds. This is done by comparing camera images with photographic databases. In each case, human operators—not machines—make the final judgment before further action is taken. Images from close circuit television (CCTV) cameras are routinely used in security systems despite being of poor quality and laborious to interpret. Two-dimensional and three-dimensional MV provides methods to enhance picture quality, interpret events and monitor complex scenes. Example applications include plate number identification, people tracking, face recognition and intruder monitoring.
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Computer Vision and Machine Vision in Everyday Life( IAS148A )
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