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  In the past 10 years or so all the automakers you talked to were excited by the prospect of autonomous cars appearing on the market.   While a few major automakers have launched 'not-quite-autonomous' vehicles that can drive themselves down the highway (with a constant watch from the drivers, of course), the autonomous technology hasn't happened as experts believed. In the year 2019 globally there were around 31 million self-driving vehicles (some degree of autonomy) operating.   The number is expected to increase until 54 million 2024.   The trend patterns suggest that the market will expand by 60% even though it experienced a decline of 3% in 2020. While there are a variety of reasons why autonomous vehicles could be released longer than anticipated, one major reason is the absence of high-quality training data in terms of quantity variety, validation, and diversity.   What is the reason why training data is crucial for the development of autonomous vehicles? The import

  When it comes to vehicle security and assistance for drivers, ADAS cameras are pivotal. How do they function and why is calibrating vital? This article we'll explain the fundamentals of ADAS camera functions and also explain the importance of calibration for precise results and security. What exactly is the definition of an ADAS camera? A driver assistance system that is advanced (ADAS) camera a camera in the automotive which collects data in order to aid drivers with specific tasks, like the ability to keep a lane clear and avoid collisions.   Depending on the model of the vehicle and the features it has it is possible to have side-, forward- mounted, or rear-mounted ADAS camera sensors. The cameras that are facing forwards are most commonly used kind that make up an ADAS camera, but rear and side mounted cameras are gaining popularity.   The forward-facing ADAS camera are attached on an inside part of the car's windshield, close to the rear-view mirror.   The majority of au

The artificial intelligence field often referred to as cognitive computing or machine learning has recently become extremely well-known.   The rapid growth in the field of "deep learning" technology in the last several years has proved significant in a variety of sectors.   The major companies in the field of industry like Google, Microsoft, Facebook, IBM and many others have made huge amounts of capital into the field of artificial Intelligence.   Machine learning has been exploding on the scene due to the advancement in emerging "deep understanding" technology. Deep learning developments can have implications for the security industry in general also.   For video dataset analytics, for instance deep learning has shown promise of improving some difficult problems, though more work is required.   This article will discuss the advancing field of deep learning as well as its possible impact on security and surveillance market. The evolution of deep learning The area

  The advancement of technology over time has improved significantly.   Incredibly amazing things are now visible in real life as objects.   The tiny tiles that we have in our smartphones are an excellent illustration of this.   These applications can be used to serve a variety of purposes.   One example of this technological advancement that is a part of technology Deep Learning.   A majority of today's applications are built on this type of technology. Let's first learn about deep learning.   It is an artificial intelligence (AI) process that is akin to the human brain's process of processing patterns and dataset for machine learning to help make decisions.   This is an AI subset within machine learning which employs neural networks to teach in a non-supervised manner from unlabeled, unstructured data.   Deep neural neural or deep neural networks are different terms used to describe the same thing. Deep learning applications include self-driving vehicles digital assistan