Using videos to market your products, services and events can be an effective way to reach your customers, but it can also be hard to do effectively. In order to harness the full power of video, you need video analytics so you can determine what’s working, what isn’t and how you can better appeal to your audience in the future. This guide from our sister site gives you the definitive breakdown of video analytics and machine learning, so you can start using it in your business right away!
Introduction
Video analytics are a powerful tool for enhancing video content by analyzing it and extracting information from it. Video analytics can be used in both video surveillance and mobile app development, among other things. Here’s what you need to know about them.
They use these devices for everything: shopping, banking, dating (yes, really), watching videos—you name it. In fact, according to data compiled by eMarketer in 2017, there were 5 billion mobile video views every single day around that time—and that number is expected to double every year until 2021. That means we will soon be reaching 10 billion views per day!
Why Machine Learning?
Machine learning is rapidly becoming a tool for all businesses, as it can be applied to almost any problem. It allows your software not only learn but also adapt based on new data and events in real time. However, its application isn’t limited to more traditional products like image search or recommendation engines.
Video Analytics is an increasingly popular area of interest in machine learning, which is leading many companies to look at what video analytics solutions are out there today. Unfortunately for both mobile app developers and businesses, there aren’t that many options available yet.
Types of ML
It’s helpful to think of machine learning as falling into two categories: supervised and unsupervised. In supervised, or classification, ML, a program is trained on previously labeled examples (training set) in order to learn how to label new examples (test set).
The test set could be comprised of new videos or new parameters. For example, if you wanted a program that recognizes pedestrians crossing a street, you would feed it images that are labeled crossing pedestrians and images that are labeled not crossing pedestrians. Once it has learned these labels based on your training set, you can then test its results against your test set. It does well? Great! You can deploy it to video analytics software for actual use.
Data Preparation
When using machine learning for video analytics, we must have both data and computation at our disposal. We need a training set—either real time or historical—and a computing platform that allows us to solve our problems. Also, we need large quantities of data. For our use case, we’ll be utilizing Apache Spark as our platform of choice due to its easy integration with AWS (Amazon Web Services) S3 and Hadoop clusters. As far as computer vision algorithms go, deep learning models tend to require more computing power than traditional approaches such as Convolutional Neural Networks (CNNs), but they also provide better results.
Building the Model
By gathering a massive amount of mobile video analytics data, machine learning can then be used to analyze and classify motion events. You can categorize these events into four main categories: Enter, Exit, Change and Stability. It’s important not only for businesses but consumers too. When your customers leave a store, it can tell you how long they stayed in your store, what type of products were purchased, whether or not they liked your products. All of these metrics are important information that helps your business make more money while providing better service to their customers.
Conclusion
There is a new world of analytics ahead. Analytic algorithms have been using machine learning techniques to analyze and make predictions based on raw data. This is known as advanced analytics and some simple examples include things like predictive sales models, fraud detection in credit card systems, or video detection of specific objects. Advanced analytics adds artificial intelligence (AI) based predictions that can be used in applications such as mobile app development tools and internal IT tools like server monitoring systems.
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