Articles

Applying Convolutional Networks for Better Video Streaming Performance

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In the wake of COVID, video streaming is no longer a fun diversion. Organizations are depending on it to keep their workforce moving… and parents are counting on it to keep their kids from going into all-out rebellion mode during lockdowns. We’re all familiar with the hiccups we experience using streaming platforms, so the application of Deep Learning to video encoding and streaming promises to be an interesting frontier. 

Convolutional Neural Networks (CNNs) are a form of Deep Learning – machine learning designed to mimic the human brain by creating multiple layers of ‘neuron’ connections based on weighted probabilities – that is commonly used in image recognition. Each neuron represents a combination of features from a dataset, which are activated for prediction through sigmoid, threshold and rectifier functions. 

Source de l’article sur DZONE

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Vector Similarity Search Hides in Plain View

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Imagine a room with a wall of screens displaying closed-circuit video feeds from dozens of cameras, like a security office in a film. In the movies, there is often a guard responsible for keeping an eye on the screens that inevitably falls asleep, allowing something bad to happen. Although intuition and other distinctly “people skills” are useful in security, most would agree that the human attention span isn’t well-suited for always-on, 24/7 video monitoring. Of course, footage can always be reviewed after something happens, but it’s easy to see the security value of detecting something out of the ordinary as it unfolds.

Several cameras capturing different scenes.
Cameras capture our every move, but who watches them?

Now imagine a video artificial intelligence (AI) application capable of processing thousands of camera feeds in real-time. The AI constantly compares new footage to historical footage, then classifies anomalous events by their threat level. Humans are still involved, both to manage the system as well as review and respond to potential threats, but AI takes over where we fall short. This isn’t a hypothetical situation: from smart police drones to intelligent doorbells sold by Amazon and Google, AI-powered surveillance solutions are becoming increasingly sophisticated, affordable, and ubiquitous.

Source de l’article sur DZONE

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Deep Learning for Manufacturing: Overview and Applications

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Deep learning

Introduction to Deep Learning for Manufacturing

Before getting into the details of deep learning for manufacturing, it’s good to step back and view a brief history. Concepts, original thinking, and physical inventions have been shaping the world economy and manufacturing industry since the beginning of the modern era, i.e. early 18th century.

Ideas of economies-of-scale by the likes of Adam Smith and John Stuart Mill, the first industrial revolution and steam-powered machines, electrification of factories and the second industrial revolution, and the introduction of the assembly line method by Henry Ford are just some of the prime examples of how the search for high efficiency and enhanced productivity have always been at the heart of manufacturing.

Source de l’article sur DZONE

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