Augmented Reality (AR) is a digital media that allows the user to integrate virtual context into the physical environment in an interactive multidimensional way. AR software derives information about the surrounding environment from cameras and sensors. Implementing AI enhances the AR experience by allowing deep neural networks to replace traditional computer vision approaches, and add new features such as object detection, text analysis, and scene labeling.
AR and VR are often discussed interchangeably or in the same circles, but it's important to distinguish their differences. So first, let's establish what these terms mean.
- AR: Augmented Reality - this is any technology that augments your real life experience with the digital one - you can show your kids a wombat on the floor of their room through the screen on your smartphone.
- VR: Virtual Reality - this term is used to talk about fully virtual experiences, with the help of special goggle-like devices.
- AI: Artificial Intelligence - AI is different from VR and AR, because it doesn't work on the level of user's perception ,it is a technology under the hood of the product you use. This is how Spotify knows what to play next after your favourite song. It is the gathering and processing of vast amounts of information to make the user experience better and tailored to the user.
Power of Augmented reality with Artificial Intelligence
Augmented reality (AR) is quickly becoming one of the biggest game-changers for businesses, profoundly transforming brand engagement. To create these compelling and powerful customer-centric experiences, AR doesn’t act alone. The underlying technology that makes the new dimensions and immersive experiences possible is artificial intelligence (AI).
AI is the key to enabling AR to interact with the physical environment in a multidimensional way. Object recognition and tracking, gestural input, eye tracking, and voice command recognition combine to let you manipulate 2D and 3D objects in virtual space with your hands, eyes, and words.
AI enables capabilities like real-world object tagging, enabling an AR system to predict the appropriate interface for a person in a given virtual environment. Through these and other possibilities, AI enhances AR to create a multidimensional and responsive virtual experience that can bring people new levels of insight and creativity.
Types of Data Annotation in Augmented Reality include:
1. Object labeling
Object labeling utilizes machine learning classification models. When a camera frame is run through the model, it matches the image with a predefined label in the user’s classification library, and the label overlays the physical object in the AR environment. For example, Volkswagen Mobile Augmented Reality Technical Assistance (MARTA) labels vehicle parts, and provides information about existing problems and instructions on how to fix it.
2. Object detection and recognition
Object detection and recognition utilize convolutional neural network (CNN) algorithms to estimate the position and extent of objects within a scene. After the object is detected, the AR software can render digital objects to overlay the physical one and mediate interaction between the two. For example, IKEA place ARKit application scans the surrounding environment, measures vertical and horizontal planes, estimates depth, and then suggests products that fit the particular space.
3. Text recognition and translation
Text recognition and translation combines AI Optical Character Recognition (OCR) techniques with text-to-text translation engines such as DeepL. A visual tracker keeps track of the word and allows the translation to overlay the AR environment. Google Translate offers this functionality.
4. Automatic Speech Recognition
Automatic Speech Recognition (ASR) uses neural network audiovisual speech recognition (an algorithm that relies on image processing to extract text). Specific words trigger an image in the library labeled to fit the word description, and the image is projected onto the AR space. An example is the Panda sticker app.
Importance of Data Annotation for AR
Innovative AR and VR experiences start with high-quality training and validation When it comes to overcoming AR and VR challenges, quality AI training data matters. 98 percent accuracy with semantic segmentation is needed to even remove the background for an AR application. And, without a precise understanding of motion or accurate perception of the environment, the realism of AR and VR applications is lost, and the user’s experience is greatly impaired. For example, before you can eliminate hand controllers, you need to first understand what your hands and fingers are trying to do i.e. point at something, grab something, wave at someone, etc., and collect data relevant to that use case.
Everything from localization and mapping, the way computers visualize the world, and semantics such as how computers understand the world as we do are all concerns that must be addressed for production level AR and VR. This is where the quality of your training data makes a difference.
TagX offers data annotation services for machine learning. Having a diverse pool of accredited professionals, access to the most advanced tools, cutting-edge technologies, and proven operational techniques, we constantly strive to improve the quality of our client’s AI algorithm predictions. With our high-quality training data, you will create the accurate AI models needed to provide a seamless and authentic AR/VR experience.
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