Video analytics, sometimes referred to as video content analysis or intelligent video analytics, has recently piqued the interest of both the academic and business communities. The significant strides achieved in deep learning have allowed AI video analytics to automate operations that were previously the sole domain of humans.
Recent advancements in video analytics have changed the game for applications like automated license plate identification, people counting at events, and other more well-known uses like facial recognition and smart parking.
Real-time vehicle detection by CCTV security cameras allows AI for surveillance to identify certain occurrences, such as automobile accidents, and send out notifications in response.
What Is Intelligent Video Analytics?
Automatically identifying temporal and spatial events in videos is the primary objective of video analytics. A suspicious-looking movement and disregarded traffic signals can result in the quick arrival of flames and smoke.
Examples of what a video analytics solution can detect include:
Fall Detection
In addition to identifying a specific item as a human, video analytics software is intended to capture a human’s fall and save it for later video forensics analysis. A liability lawsuit’s outcome may depend on how and why the victim falls.
Area Obstruction Detection
When an open space within the frame is obstructed for a long time, AI video analytics may be configured to detect this. The software is designed to recognize obstructions in the region it is watching visually.
Applications
The general public is well aware of several applications in the field of video analytics. The AI for surveillance is one such instance; it has been practiced for around 50 years. The concept is straightforward in theory: strategically place cameras to allow human operators to regulate what takes on in a space, such as a room or a public area.
In reality, it is a chore that is everything but easy. Typically, an operator is in charge of many cameras, and studies have shown that increasing the number of cameras that must be watched over has a negative impact on the operator’s performance.
To put it another way, even if there is a lot of technology accessible and it is producing signals, there is a bottleneck when it comes to processing those signals because of human error or artificial limitations.
Industrial Application
Healthcare
Video analytics allows us to do more than just make surveillance chores easier by allowing us to use the data gathered to enhance our business objectives.
For instance, a video analytics solution might notify the staff when a patient has not been checked on in accordance with their demands. It may be quite helpful to analyze patient and visitor traffic to find strategies to reduce wait times while maintaining open access to the emergency area.
Another use that offers considerable value is home monitoring for seniors or those with health difficulties. For instance, falls are a significant source of harm and mortality among the elderly. Although they must be worn and are typically ignored by consumers, personal medical devices can detect falls.
In order to determine whether a person has fallen in real-time, an AI video analytics solution can evaluate the data from home cameras. Such a system, when properly configured, might also tell whether a person took a certain drug when they were meant to, for example.
Another field where video analytics can have a big impact is mental healthcare. To help doctors evaluate patients, systems that study facial expressions, posture, and gaze might be created. A system like this may identify emotions from body language and micro-expressions, providing physicians with unbiased data that might support existing theories or provide them with fresh vital information.
