Augmented Reality (AR) is an emerging technology trend that has potential applications across a wide range of industries. AR enhances our experience of real-world environments by inserting digitally produced information into the viewer's perception in a variety of ways. This can mean attaching specific information to particular real-world objects, or inserting a digital object into a real-world space. Increasingly artificial intelligence is being incorporated into augmented reality applications. Machine learning models have shown the capacity to improve AR experiences and increase the potential of these integrated systems.
This blog will spotlight some of the interesting AR use cases that are being enabled by computer vision based AI models. In order to continue this productive synergy today’s AR developers need access to precise, varied training data. Therefore we will also address the ways in which innovators can access the right video and image data with the help of annotation providers like Keymakr.
Retail and furniture shopping
AR for mobile applications is allowing customers to visualize how products will look in their homes before they buy them. AR systems can convert two-dimensional objects, like furniture, into 3D models that can then be moved into place via a phone screen. AI is integrated into these emerging applications, allowing customers to instantaneously locate products that they have photographed in online catalogues. AR technology can also improve the retail shopping experience. Apps are in development that can help shoppers find products more quickly in brick and mortar shops. AI based object detection models can identify specific products and guide customers as efficiently as possible.
AR for retail and furniture shopping is being streamlined with AI models. However, these models require large amounts of varied image data in order to correctly identify whole product ranges. Outsourcing image annotation often enables AR companies to refine their systems without the workload burden of data collection and labeling.
Fashion and cosmetics
Virtual wardrobes are another AR application that benefits from computer vision AI innovation. These systems allow customers to virtually try on clothes before they buy them. Integrated AI systems are required to identify the body shape and movements of shoppers as well as the type of clothing item that they wish to try on. The cosmetics industry is also investing in AR. New technologies give people the chance to test how particular products will look without having to visit a retailer. AI models are able to learn the shape of an individuals’ face and how they like to wear their makeup. This adaptability improves the functionality of the AR applications significantly.
Image annotation is essential for face recognition technology of this kind. Mapping the contours of human faces requires attention to detail at the annotation stage. By identifying key facial points annotators can create data sets that reflect the diversity of human faces, leading to a more consistent end product.
AR can function as a very capable guide, overlaying complex environments with important information, allowing for ease of movement. Digital concierge services are a specific example of this technology in action. These applications allow visitors to navigate through hotels and conference centres, showing them which meeting rooms are available or the direction to their next scheduled appointment.
This useful technology requires AI models that can interpret complex moving environments. This often means training with annotated video data. Accurate frame by frame annotation is often a significant challenge for AI companies. Experienced annotation services can leverage proprietary tools to speed up creation of this vital data.
Annotation providers support augmented reality projects
Keymakr is collaborating with innovators in AI and creating high quality training datasets.