Could AI and predictive analytics advance real-time video analytics?

Video analytics technology has become a game-changer for today's industry as well as public service entities. It is an advanced technology that leverages deep learning advancements and processes digital video signals by using a unique algorithm to perform security-related tasks. Video analytics significantly provides a powerful security means to critical infrastructures, identifying impostors, tracking people or objects, and detecting behaviors. Artificial intelligence technologies, particularly the developments of deep learning, and predictive analytics have revolutionized video analytics and intelligent surveillance capabilities.

The amount of data generated by video surveillance solutions undoubtedly is enormous. Handling and processing such data in a fraction of time using manpower alone is crucial. Video analytics emerges as a constructive asset making generated video data more meaningful and valuable all in real-time.

Artificial Intelligence in Video Analytics

Artificial intelligence and deep learning provide automated solutions to efficiently assess voluminous data produced by videos, resulting in quicker outcomes. These technologies can be used for facial recognition and allow video analytics software to examine facial data more quickly and precisely. Trained on deep neural networks, a video analysis system can mimic human behavior and identify given objects in an image using computer vision techniques.

The use of AI in video analytics allows systems to communicate with each other and make decisions to detect suspicious activities and predict them before they take place. As a vital subdivision of AI, deep learning detects anomalies, improves accuracy and video scene understanding for intelligent surveillance.

Today, video analytics is being used in a wide range of industries, including healthcare to ensure patient safety, smart cities to maintain traffic management, smart parking and city surveillance, retail to understand customers' behaviors and interests, and security.

Role of Predictive Analytics in Video Analytics

Recent advances in video analytics, video management systems and IP cameras have significantly demanded powerful processing capabilities and advanced analytics. Using predictive analytics provides accurate detection of abnormalities in people's behaviors and triggers alerts. It facilitates enhanced analysis of a tremendous amount of video data generated every day. Predictive analytics typically uses information gleaned from vast data sources such as surveillance, visitor management, incident management and other systems. It then analyzes the information against existing behavioral models and takes into account to effectively predict the likelihood of a similar event in the future.

For instance, many retail stores today have deployed video analytics to understand customers' buying behavior, identify and thwart theft and fraud, and deliver an enhanced shopping experience. By integrating predictive analytics, retailers can be able to foresee any susceptible activities and avoid them before happening. They can drive products and services' promotions that might be of interest to customers. They can even reduce customers' wait-times using queue management analytics and optimize their workforce in a time of crowd.

The adoption of artificial intelligence, deep learning, predictive analytics and wireless features like RFID is likely to bolster the demand for video analytics across industries. The market for video analytics is set to reach US$11.5 billion in 2025, from US$4.9 billion in 2020 at a CAGR of 10%.