You’ve probably seen this scenario play out on a police procedural show on television: A crime has been committed and officers are tasked with looking through security footage to see if any of it was caught on camera.
On TV, they can cut away to commercial and have the answer back as soon as they return. In real life, however, analyzing huge quantities of video data is a task that’s rarely accomplished effectively by human operators. There’s just too much data to sift through, and the cost for the man hours required is too high.
But that problem is being overcome with machine learning and video analytics.
Video analytics is the process of extracting information, meaning, and insights from video footage. And video analytics can do everything that image analytics can do, plus a bit more.
Whereas image analytics looks at a still image – be it a photograph or a medical scan – and seeks to find patterns and anomalies, or identify faces in pictures, video analytics also can measure and track behavior.
Traditionally, video data was only really gathered on closed-circuit TV for security purposes to monitor retail or business premises for theft, malicious damage, or employee wrongdoing. The purpose of the video footage was to protect the business and provide evidence if something happened. If nothing happened, the recordings would be erased so the tape or digital hard drive could be re-used over and over again. All that data wasn’t saved because there was too much of it and there was no way to use it.
But all that has changed. Increases in storage capability and new analytics techniques mean that all that video footage is now very useful. And we have an awful lot of it. Nearly every person on the street has a cell phone equipped with a video camera. Three hundred hours of video are uploaded to YouTube every minute. And even inexpensive security cameras now come with facial-recognition software.
How Video Analytics is Being Used
Video analytics can be used for identification (face recognition), behavior analysis, and situational awareness. Businesses use video analytics if they want to know more about who is visiting their store or premises, and what those people are doing when they get there. Facial recognition can be used to help maintain security, but it also can be used to find out more about a business’ customers.
And because video data is dynamic, not static like image data, you can also use it to monitor your customer’s behavior and learn more about how they react to offers, etc. For example, you can collect data from different closed-circuit TV cameras in a retail environment and analyze the footage to see how your customers behave and how they move through the store. This data can help you see how many people stop at a particular product display or offer, how long they engage with it, and whether or not it is working and converting into sales.
Video analytics are also being used in other fields, including law enforcement, security, and even marketing. Body cams for police officers are a great example of this. The New York City police department alone generates more than a million hours of video footage per week; that’s a lot of data that could be useful to the department. But much of this data is never reviewed, even when it could provide investigative leads.
Proper video analytics could add rich tagging and indexing to the video to aid in future searches. It also can search video from certain time periods and for individuals with certain characteristics to develop leads; and even can be taught to recognize patterns and predict vulnerabilities.
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