In the past few years, video analytics, also known as video content analysis or intelligent video analytics, has attracted increasing interest from both industry and the academic world. Thanks to the enormous advances made in deep learning, video analytics solutions has introduced the automation of tasks that were once the exclusive purview of humans.
Recent improvements in video analytics have been a game-changer, ranging from applications that count people at events, to automatic license plate recognition, along with other more well-known scenarios such as facial recognition or smart parking.
In this guide, you’ll discover the basic concept of video analytics, how it’s used in the real world to automate processes and gain valuable insights, and what you should consider when implementing an intelligent video analytics solutions in your organization.
What is intelligent video analytics?
The main goal of video analytics is to automatically recognize temporal and spatial events in videos. A person who moves suspiciously, traffic signs that are not obeyed, the sudden appearance of flames and smoke; these are just a few examples of what a video analytics solution can detect.
Real-time video analytics and video m
Usually, these systems perform real-time monitoring in which objects, object attributes, movement patterns, or behavior related to the monitored environment are detected. However, video analytics can also be used to analyze historical data to mine insights. This forensic analysis task can detect trends and patterns that answer business questions such as:
When is customer presence at its peak in my store and what is their age distribution?
How many times is a red light run, and what are the specific license plates of the vehicles doing it?
Industry applications
Healthcare
Real-world example
Smart cities / Transportation
Retail