For today’s market, as well as public sector agencies, video analytics technology has been a game-changer. It is an advanced technology that leverages developments in deep learning and processes digital video signals to perform security-related tasks using a special algorithm.
Video analytics provides vital infrastructures with powerful security means, recognizing impostors, monitoring individuals or artifacts, and detecting behaviors. Video analytics and intelligent surveillance capabilities have been revolutionized by artificial intelligence technologies, particularly the development of deep learning and predictive analytics.
Undoubtedly, the amount of data produced by video surveillance solutions is immense. It is vital to manage and process such information in a fraction of time using manpower alone. Video analytics is a proactive asset that makes video knowledge produced in real-time more meaningful and useful.
Artificial intelligence and deep learning provide automated solutions to analyze voluminous data produced by videos effectively, resulting in faster results. These technologies can be used for facial recognition and allow facial data to be analyzed more easily and precisely by video analytics software. Trained on deep neural networks, a video analysis system may use computer vision techniques to imitate human behavior and recognize given objects in an image.
In video analytics, the use of AI enables systems to communicate with each other and make decisions to identify and anticipate suspicious activities before they take place. Deep learning, as a critical subdivision of AI, detects anomalies, increases precision and comprehension of the video scene for intelligent monitoring.
In a wide variety of industries, video analytics are now used, including healthcare to ensure patient safety, smart cities to sustain traffic management, smart parking and city monitoring, retail to understand the habits and desires of consumers, and protection.
Recent developments in video analytics, video management systems, and IP cameras have significantly needed advanced analytics and powerful processing capabilities. Using predictive analytics provides effective identification of anomalies and causes warnings in people’s behaviors. This allows better analysis of a huge amount of video data generated every day.
Usually, predictive analytics uses data collected from large data sources, such as monitoring, visitor control, incident management, and other systems. It then analyzes the data against current behavioral models and takes into account the possibility of a similar occurrence in the future in order to accurately predict it.
For example, today, many retail stores have implemented video analytics to understand the purchasing habits of consumers, detect and thwart theft and fraud, and provide an improved shopping experience. Retailers would be able to anticipate any susceptible events and stop them before they happen by incorporating predictive analytics. They will push promotions of goods and services that may be of interest to clients. Using queue management analytics, they can also reduce customer wait times and maximize their workforce in a time of crowds.
The introduction of artificial intelligence, deep learning, predictive analytics and wireless features such as RFID is likely to increase the demand across industries for video analytics. The video analytics market is set to reach 11.5 billion US dollars in 2025, from 4.9 billion US dollars in 2020 at a 10 percent CAGR.
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An excellent article on how AI and predictive analytics have completely transformed the video surveillance sector. Organisations can now proactively monitor and respond to events in near real-time, rather than rely on video purely as a deterrent or historical record of an event.