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Businesses have had several transformation breakthroughs in the last two years as a result of the epidemic that was expected to happen in the following five years. The rate of technological adoption will continue to rise. It mostly comprises artificial intelligence (AI) and intelligent industrial automation. Even though businesses are still learning how to use various AI technologies, computer vision will continue to offer up new technical vistas for the hyper-digital dawn. Video intelligence, one of the most talked-about computer vision technologies, has a lot of practical applications. The computer vision industry is expected to reach USD 49 billion by 2022, according to Forbes.
As a crucial business enabler, computer vision delivers visual or video intelligence, which is used by numerous organisations throughout the world to execute safety measures. Best-of-breed AI combined with video event detectors for health, safety, and the environment (HSE) supercharges cameras to automate monitoring and analysis. The video intelligence detects events such as the lack of a hardhat, safety vest, or face mask, as well as anything else that leads to safety procedure violations.
Computer vision might be a lifesaver for industries such as food and beverage, automotive, manufacturing, and retail, which have seen dwindling manpower and profits. At a low cost, computer vision enhances visual inspection to obtain improved quality, accuracy, and flexibility.
The most-talked topic of computer vision- edge computing– a distributed computing framework that brings enterprise applications closer to data sources such as IoT devices or local edge servers. It enables businesses to process and analyze a large amount of data more quickly at the site of collection. This leads to gathering actionable real-time analysis, insights, and calculations. Software deployed on edge computing automates and accelerates the cycle time and monitoring of labor-intensive processes. It quickly connects to cameras and video management systems (VMS) to detect and prevent safety breaches in real-time.
Computer vision improvements assist in automating data labeling and future-proofing training operations. Various training methods now in use by organizations will seamlessly feed data pipelines, allowing computer vision applications to be activated faster. It will speed up data processing while reducing or eliminating mistakes. The year 2022 will be remembered as the year when we first saw end-to-end automatic annotation for photos and movies.
Anomaly detection has been proved to be a useful method for spotting financial transaction mistakes. Experts see it as a "future trend in computer vision." When correctly trained, machine learning-based models excel in detecting anomalies in payment processes and preventing suspected fraud. Anomaly detection now provides another layer of security, lowering the likelihood of a payment being hacked. It helps not only with general operations but also with providing outstanding services to customers and end-users.
Despite the impact of digital disruption and the use of modern technological tools, banking remains vulnerable to cybercrime and theft. Furthermore, assessing and processing massive and sensitive data is a time-consuming procedure that prevents bank operations from outperforming other fields. Optical character recognition (OCR) technology, a subset of computer vision, collects and extracts large amounts of financial or banking data from a variety of documents or folders rapidly and efficiently.
With an explosive demand for video analytics solutions in 2022, businesses would be seeking robust software that is being trained using 300+ different parameters for highly sensitive tracking with minimum hardware or infrastructure upgradation expenses. Computer vision smartens up the video analytics solutions to get robust video insights with minimum or no falls alarms. This software is customizable, scalable, and helps to save time and cost.
Computer vision is a game-changer in the banking industry. OCR technology, a subtype of computer vision, is a Swiss army knife for swiftly and effectively capturing and retrieving data. Bank staff can manage large volumes of data more easily because of technology. OCR technology gathers and extracts data from a wide range of documents, freeing up staff time for other important tasks. Computer vision technology is rapidly being used by banks to improve client experiences and back-office activities.
Vision analytics systems driven by AI recognise events and deliver warnings. They don't, however, have a comprehensive system in place to evaluate the occurrence in detail and prevent it from happening again. When integrated with a machine learning model, computer vision can explain an occurrence or a forecast without requiring human interpretation. In 2022, computer vision will be integrated with a variety of other powerful technologies, tools or frameworks to reveal the logic behind the computer vision model's behavior and performance.
