Microsoft unveiled new AI capabilities at its annual Ignite conference. One of the most intriguing is the "PeopleLens" AI system.

PeopleLens is a platform that leverages computer vision algorithms to assist blind individuals in interacting with their social environments. The system recognizes and interprets things in the user's environment and communicates those facts to the user in an understandable manner.

Furthermore, PeopleLens offers a whole new world of possibilities for blind individuals. They may now participate in conversations, navigate their environment, and experience the world in previously inconceivable ways.

This post will learn about PeopleLens, its operating concept, benefits, and probable consequences.

What exactly is PeopleLens, and how was it created?

Microsoft's PeopleLens is a computer vision system that uses machine learning algorithms to assist blind people in interacting with their social environments. The project's objective is to help those with vision impairments become more self-sufficient and socially engaged.

Image courtesy: Microsoft

Over two years, a team of Microsoft engineers and computer scientists worked on PeopleLens. The objective was to develop a machine learning system that would assist blind people in navigating their social environments by recognizing persons and objects in photographs.

The researchers employed a dataset of photos labelled with the presence of people and objects. They then trained a computer vision model using deep learning algorithms to recognize these labels in new images.

How does it work?

Microsoft's PeopleLens assists blind individuals in seeing. The technology utilizes computer vision algorithms to help blind individuals comprehend their social environment. PeopleLens identifies individuals within a scene and then offers contextual information, such as their name and location.

Image courtesy: Microsoft

The PeopleLens assists the child who is wearing it to develop a mental map of their immediate social surroundings. Since the PeopleLens reads out the names of identified persons in spatialized audio, the youngster understands their friends' relative positions and distances. As illustrated by the overlays on the top photographs in this snapshot of the PeopleLens development environment, the system receives images and processes them using computer vision techniques. 

Furthermore, PeopleLens is a platform comprising two components: a wearable gadget and a cloud-based service. The device gathers photographs of its surroundings and sends them to the cloud-based service, where machine learning algorithms process them. This data constructs descriptions of the immediate surroundings transmitted back to the wearable device.

Additionally, the system can distinguish between various objects in a scene, such as a chair or a table. PeopleLens will send this information to the blind person through Braille or audio.

Benefits

The PeopleLens is a game-changing device with privacy in mind. Among the algorithms that underpin the system is facial recognition of registered users. A person registers by photographing themselves numerous times with their phone hooked to the PeopleLens. However, instead of storing photographs, they keep them as a vector of integers that depict a face. These vectors are distinct from those utilized by other systems, and hence recognition by the PeopleLens does not imply recognition by other methods. In addition, the technology does not collect video or personally identifiable information, guaranteeing that users cannot utilize the photographs maliciously.

Conclusion

Microsoft's PeopleLens assists blind individuals in seeing. It interprets photos and movies using computer vision algorithms and then converts the data to braille or audio output. In addition, PeopleLens enables blind individuals to interact more freely with their social environment.

Moreover, the PeopleLens is a tool for blind children and young adults to find friends; however, it may also help instructors and parents to develop social competence and confidence. The advantages of this approach are enormous, and it has a plethora of possible applications. We're looking forward to seeing how PeopleLens goes in the future.