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A Guide to Video Analytics: What You Should Know

In the last couple of years, video analytics, also known as video content analysis or intelligent video analytics, has attracted immense interest from both industry and the academic world. With the popularization of deep learning, video analytics has brought about the automation of tasks that were initially exclusive purview of humans.

Recent transformations in video content analytics have been a game-changer, ranging from applications that monitor traffic jams and alert in real-time, to those that analyze customers’ flow in retail to maximize sales, together with other more well-known scenarios such as smart parking or facial recognition.

In this quick guide, we take you through a few important things to know before using video content analytics to your advantage.

What is Intelligent Video Analytics?

It is important to note that the key behind video analytics is to automatically detect temporal and spatial events in videos. A person who moves around suspiciously, traffic signs that are not obeyed, the sudden appearance of flames and smoke, are merely but some of the few examples of what a video analytics solution can do.

In most instances, these systems perform real-time monitoring in which objects, object attributes, movement patterns, or even behavior related to the monitored environment are detected. Despite all that, video analytics can also be relied upon in analyzing historical data to mine insights. This has the potential to detect trends and patterns that answer business questions.

Machine learning, and to be precise, the spectacular development of deep learning objective, has immensely transformed video analytics. The use of Deep Natural Networks (DNNSs), has made it possible to train video analysis systems that mimic human behavior, leading to a paradigm shift.

In the new paradigm, models based on deep learning can identify the exact area of an image in which license plates appear. With this info, OCR is applied only to the exact region in question, leading to reliable results.

Well, video content analytics is now applicable in a wide array of industries. Among the most popular ones include healthcare, transportation, retail, sports, and security, to name a few. Understanding the different industry applications of video analytics goes a long way in helping you put it into the best of use cases.

It is essential that you factor in the pros and cons of video analytics before making the bold decision to put it into use in your business or organization.

Lisina Jordan

"Lisina Jordan is a data scientist and machine learning expert with a passion for using data to solve complex problems. He has worked in various industries, including finance, healthcare, and retail, and has a wealth of experience in data analysis, modeling, and visualization. Lisina Jordan's blog is a valuable resource for anyone interested in data science and machine learning, and he regularly shares insights and tutorials on the latest technologies and techniques. In his free time, he enjoys playing chess and reading science fiction novels. "