The importance of computer vision is in the problems it can solve. It is one of the main technologies that enable the digital world to interact with the physical world.
CV applications are widely found in automotive industry. It enables self-driving cars to make sense of their surroundings. Cameras capture video from different angles around the car and feed it to computer vision software, which then processes the images in real-time to find the extremities of roads, read traffic signs, detect other cars, objects and pedestrians. The self-driving car can then steer its way on streets and highways, avoid hitting obstacles, and (hopefully) safely drive its passengers to their destination.
Computer vision also plays an important role in facial recognition applications, the technology that enables computers to match images of people’s faces to their identities. Computer vision algorithms detect facial features in images and compare them with databases of face profiles. Consumer devices use facial recognition to authenticate the identities of their owners. Social media apps use facial recognition to detect and tag users. Law enforcement agencies also rely on facial recognition technology to identify criminals in video feeds.
Computer vision also plays an important role in augmented and mixed reality, the technology that enables computing devices such as smartphones, tablets and smart glasses to overlay and embed virtual objects on real world imagery. Using computer vision, AR gear detect objects in real world in order to determine the locations on a device’s display to place a virtual object. For instance, computer vision algorithms can help AR applications detect planes such as tabletops, walls and floors, a very important part of establishing depth and dimensions and placing virtual objects in physical world.
Online photo libraries like Google Photos use computer vision to detect objects and automatically classify your images by the type of content they contain. This can save you a much time that you would have otherwise spent to add tags and descriptions to your pictures. Computer vision can also help annotate the content of videos and enable users to search through hours of video by typing in the type of content they’re looking for instead of manually looking through entire videos.
Computer vision has also been an important part of advances in health-tech. Computer vision algorithms can help automate tasks such as detecting cancerous moles in skin images or finding symptoms in x-ray and MRI scans.
Computer vision has other, more nuanced applications. For instance, imagine a smart home security camera that is constantly sending video of your home to the cloud and enables you to remotely review the footage. Using computer vision, you can configure the cloud application to automatically notify you if something abnormal happens, such as an intruder lurking around your home or something catching fire inside the house. This can save you a lot of time by giving you assurance that there’s a watchful eye constantly looking at your home. The U.S. military is already using computer vision to analyze and flag video content captured by cameras and drones (though the practice has already become the source of many controversies).
Taking the above example a step further, you can instruct the security applications to only store footage that the computer vision algorithm has flagged as abnormal. This will help you save tons of storage space in cloud, because in nearly all cases, most of the footage your security camera captures is benign and doesn’t need review.
Furthermore, if you can deploy computer vision at the edge on the security camera itself, you’ll be able to instruct it to only send its video feed to the cloud if it has flagged its content as needing further review and investigation. This will enable you to save network bandwidth by only sending what’s necessary to the cloud.