Artificial Intelligence can flag people based on their clothing or behavior, detect people’s emotions, and find people who are “extraordinary”.
It was the surveillance cameras that were inactive. Maybe they just recorded it, and no one else watched the video unless they needed to. The frustrated guard sees a dozen different screens, scanning for interesting ones. In both cases, the video is only stored for a few days because storage is expensive.
Growing up, none of it was real. Recent developments in video analytics fueled by artificial intelligence techniques such as machine learning enable computers to view and interpret surveillance videos with human intelligence. Identity technologies make it easy to automatically identify who is in the videos. In the end, the cameras themselves became cheaper, ubiquitous and even better; Cameras mounted on drones can effectively see the entire city. Computers can watch all videos without human problems such as distraction, fatigue, training or need to pay. The result was a level of intelligence that was impossible some years ago.
The ACLU report published Thursday that “Dawn of Robot Surveillance” does not record ourselves as “AI-aided video surveillance,” but judges us based on their perception of our actions, emotions, skin color, clothing, voice, and more.
Let’s take the technologies one at a time. First: video analytics. Computers are improving in detecting what is happening in the video. It is easy to identify when a person or vehicle enters a prohibited area. Modern systems become alert when someone is walking in the wrong direction — for example, an exit only passing through the corridor. They can count people or cars. They can identify when the luggage is left unattended or when the luggage is taken away. They can detect when someone is breaking into an area, lying down, or running. Increasingly, they can identify the specific actions of individuals. Amazon’s cashier-less stores rely on video analytics to figure out when someone has taken an item off the shelf and not put it back on.
Slightly recognizing actions, video analytics enable the computer to understand what is happening in the video: they can flag people based on their clothing or behavior, identify people’s emotions by body language and behavior, and find people who are “abnormal” on a per-person basis. Around them. The same Amazon store cameras can analyze customer sentiment. Other systems can explain what is happening in the video scene.
Computers can also detect people. AIs are improving in identifying the people in those videos. Facial recognition technology is constantly improving, facilitating the enormous storage of tagged photographs we provide to Facebook and other social media sites, as well as government-issued photos in the process of issuing ID cards and driver’s licenses. The technology is already there to automatically detect everyone who “sees” the camera in real-time. Even without video recognition, we can find unique information that is constantly transmitted through smartphones that carry with us everywhere, or through our laptops or Bluetooth-connected devices. Police have been tracking phones for years, and this method can now be combined with video analytics
5 reasons AI needs to evolve video surveillance solutions
Why video surveillance solutions
Artificial intelligence has begun to impact the industries on a large scale, but the impact is nowhere near as high as the security column. New and innovative solutions are being launched not only by establishing security vendors but also by smaller startups, and these solutions add value to a company’s security operations. Let’s discuss how AI will impact the video surveillance industry shortly.
Real-time monitoring
Basically, in the days of CCTV cameras, a video was used to live on TV screens, but very little was done to make any meaningful analysis of the security incident. In those days, video surveillance solutions were always reactive and continued to be used in large parts of the world. Most agencies for CCTV footage are only in the event of an incident or a massive threat awareness. The use of AI has completely transformed the picture through real-time monitoring of video footage with a clever analysis of how security incidents can occur and what needs to be done to prevent them from happening. Various large IT vendors have come up with AI solutions that can detect unattended items, such as bags at airports and public places, allowing security agencies to take action before it is too late. Similarly, AI-enabled surveillance solutions can monitor people rotating in retail stores and identify who is involved in a theft.
More accurate than men
CCTV monitoring is as accurate and reliable as the monitor on the screen. However, being human, operators sometimes lose crucial security credentials, which can have devastating consequences for the company. Fatigue is one of the reasons why people don’t regularly watch video footage. The advantage of AI-based solutions is that they can be trusted that machines are accurate at all times and can address human error. This ensures that no details, no information, and absolutely no threat is detected and, therefore, the company’s security is assured.
Foolproof deployment of technology such as an object, event and face recognition
AI technologies bring superior facial recognition, object recognition and event recognition capabilities, thus providing active and real-time security. This is a huge application for the police force because they need to identify criminals from the crowd in large areas.
Another interesting application is the marketing departments of large corporations where they need to identify high-net-worth customers from their stores to provide them with high-value services.
Another important way of identifying a person through faceless identification is where a person’s physical features such as height, posture, and structure can be used to identify them in a group. Also, patterns of activity can be used in different environments to isolate criminal behavior and keep neighborhood neighborhoods safe.
Image processing allows for better analysis
Although ultra-high-definition cameras are capable of capturing high-resolution images, their use as part of video surveillance solutions is limited. As a result, most of the images captured by such solutions are of poor quality, especially in low light conditions. This can serve as a barrier when doing meaningful analysis with such video footage. Artificial intelligence can help security personnel sharpen low-quality images to extract meaningful information from them. This helps to make good use of obscure images and videos for useful analysis of the eye.
Making better use of the data flood
There is a web of cameras that capture valuable security footage in key geographical locations. Though, legacy solutions didn’t allow much to be done in terms of meaningful analysis of such big data. Special software based on the latest technology is required to work on this massive data and create security alerts based on comprehensive analysis. This enables security officials to understand big data in a qualitative manner and take crucial steps to protect human beings and valuable assets.
AI biasBecause AI algorithms are built by humans, those who deliberately or inadvertently put them into the algorithm have a built-in bias.
If AI algorithms are built with a bias, or if the data in the training sets they are given to learn is biased, they may yield biased results.
This reality may lead to unintended consequences that we have become racist with non-discriminatory recruiting algorithms and Microsoft’s Twitter chatbot.
Loss of some jobsMany jobs are created by artificial intelligence, and although most jobs predict a net increase, or at least the same amount of money created to replace those lost thanks to AI technology, the jobs that machines do today are people who take over.
This will require changes in training and education programs to prepare our future workforce and help current workers move to new positions that utilize their unique human capabilities.3.
The economic benefits of increased efficiency are very clear on the profit-loss statements of businesses, but the overall benefits to society and the human condition are somewhat opaque.4.
OpenAI is looking forward to this feat for both robotic add-ons and its own AI software, which allows Docktail to learn new things using virtual simulations, before overcoming a real, physical challenge.In a performance video featuring Docktail’s new talent, the robotic arm with clumsy yet precise maneuvers leads to a complete cube solution.
“Velander has been referring to a range of robots for the past few years that have pushed the Rubik’s Cube solution beyond the confines of human hands and minds.
In 2016, semiconductor manufacturer Infineon specifically developed a robot to solve the Rubik’s Cube at superhuman speed, and the bot was able to do so in under a second.
As Wallander notes, OpenAI’s ongoing robotics work is not only about achieving superior results in narrow tasks but also because you need to develop and program a good robot.
It has been trained using software that is currently trying to be fundamental to reflect the millions of years of evolution that help children learn to use our hands naturally.
One day, OpenI’s hopes will help mankind develop the humanoid robots we know only from science fiction, robots that can safely operate in society without risk, and perform a variety of tasks in a chaotic environment such as city streets and factory floors.To learn how to solve a Rubik’s Cube with one hand, OpenIII is not explicitly programmed by Docktail to fix the toy; Free software on the Internet can do it for you.
With this Big Data Parts of Digital Future technology becoming more ubiquitous, shoppers can make informed decisions using online data and content to discover, compare and buy products from anywhere, anytime.Uses of AI on Retail Industry in 2021: Image source: hacker noonChatbots to help with customer service:AI chatbots provide an even higher level of customer service, improve searches, send notifications about new collections, and suggest similar products.
Tommy Hilfiger and Burberry have launched chatbots to help their customers navigate their collections.Price adjustments:AI applications for retail stores could help companies set prices for their products, visualizing the likely results of multiple pricing strategies.
Mood tracking will definitely help build stronger customer relationships.Big Data Trends Revolutionizing Retail and Improving Retail Analytics: Image source: ZDNetRetailers can embrace a new geo-targeting phenomenon to better target potential customers within the store.
By utilizing the in-store sales funnel, marketers can come up with the best opportunities that deliver the highest ROI from their marketing efforts.Big Data allows retailers to feed their ideas, including cut, color and price, to an algorithm that uses sentiment analysis to predict whether or not the ideas will be a hit with consumers.It also helps retailers react more quickly to competition and electronic competition, market changes, and other macro-environmental factors by recognizing early signs of growing demand or slowing down in terms of sales.
It helps to assign sku to the stores where the demand would be and price it in a way, so it would bring the maximum gross margin.AI is transforming the retail industry in 2021: Image source: itechtionsThe AI Transforming your business in the Retail Industry has seen a massive change in its base of operations in recent times, with many large brands choosing to reduce the number of on-site offerings in favor of online service.
The reason for this is simple, customers are now looking for the path of least resistance when buying their clothes, and this is best reported on the Internet.
Machine Learning Solutions enables businesses to discover new trends easily and patterns from large and unique data sets.
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