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From visual affinity-based recommendations to proctoring to public surveillance, computer vision has got so many meaningful applications that it's time to write it off as a buzzword. Enterprises around the world are honing their competitive edge by leaving the challenge of business workflow automation to image recognition software. And in this feature, we'll delve into some practical applications of this trailblazing tech.
Interruptions in activity represent one of the major causes of financial loss in enterprises. To wit, 82% of companies experienced at least one downtime over the past 3 years with US$250,000 an hour in lost revenue. In manufacturing, downtime is not uncommon due to improper equipment maintenance and related human errors.
To avoid being part of these alarming stats, prepare a comprehensive maintenance strategy with technology as its central element. Employ automated optical inspection systems equipped with CCTV cameras and high-resolution, multiple-megapixel video sensors. Use AI to accurately analyze data collected from these sources and identify potential problems in equipment performance and production lines before they even arise.
This approach will allow you to proactively monitor the quality of products you manufacture and timely address machinery defects, avoiding unplanned breakdowns and costly downtime.
Predictive maintenance can also be used outside the industrial settings. Among the places where computer vision-powered equipment and product monitoring might be instrumental are hospitals, sports facilities, retail warehouses, agricultural lands, and more.
The demand for online video content is increasing, and a recent report by Statista is clear proof. In 2018, 85% of all internet users in the USA watched video content monthly on their devices.
However, mere content delivery is not seen as a solution to the rising needs of viewers. To drive real business value out of this venture, content producers should strive to provide seamless, engaging viewing experiences.
This boils down to the fact that M&E represents another area where automated quality control might find its feet. Coupled with AI, computer vision can be used to process vast amounts of media content — to find and autocorrect audio, video, and metadata inconsistencies.
Such a workflow empowers broadcasters to timely detect and isolate anomalous content, including glitches, black screens, and artificial text. To ensure flawless content delivery, they can use either frame-level deep learning approach — with Convolutional Long Short-Term Memory (Conv-LSTM) networks at its core — or unsupervised, scene-based content processing.
If you want to take computer vision-enabled workflow automation to a new level — far beyond video content quality control — leverage it power to ensure strict regulatory compliance.
Tap into cutting-edge content orchestration to detect adult content, smoking, alcohol objectionable language, violence, racism, and other sensitive themes in a fraction of time. Automatically remove scenes inappropriate for political or religious reasons to safely deliver content in a particular region.
Reveal hidden dependencies between scenes and generate visual affinity-based content recommendations — for increased viewer engagement.
Besides, such a computer vision-fueled approach can be used to create personalized posters, generate engaging highlights, and optimize ad insertions.
Computer vision has already helped an array of businesses establish a firm foothold in today's highly competitive world. Companies are using this tech to automate equipment monitoring, improve inventory management, deliver hyper-personalized content offerings, and provide flawless viewing experiences. Are you ready to follow suit?
Yana Yelina is a Technology Writer at Oxagile, a provider of software engineering and IT consulting services. Her articles have been featured on KDNuggets, ITProPortal, Jaxenter, Singularity Hub, and Datafloq, to name a few. Yana is passionate about the untapped potential of technology and explores the perks it can bring businesses of every stripe. You can reach Yana at yana.yelina@oxagile.com or connect via LinkedIn or Twitter.
