Most technologies are developed with an inspiration of human capabilities. Most of the time, the hardest to implement capability is vision. Development of highly capable computer vision applications in an easy way requires a generic approach. In this approach, Arduino is a perfect tool for interaction with the real world. Moreover, the combination of OpenCV and Arduino boosts the level and quality of practical computer vision applications.
Computer vision is fundamental for a broad set of Internet of Things (IoT) applications. Household monitoring systems use cameras to provide family members with a view of what's going on at home. Robots and drones use vision processing to map their environment and avoid obstacles in flight. Augmented reality glasses use computer vision to overlay important information on the user's view, and cars stitch images from multiple cameras mounted in the vehicle to provide drivers with a surround or "bird's eye" view which helps prevent collisions.
Engineers have always tried to give the robot the gift of sight. So, they have to replicate the human vision process with computers, algorithms, cameras and more. In the DIY area, a Raspberry Pi is the queen of prototyping platforms. So, why not to use it in computer vision applications. The projects started coming fast and furious for navigation, localization, recognition, classifications, monitoring, reading and more.
Retail innovations like Amazon Go have captured the headlines recently, but over the past few years, Computer Vision applications and technologies have been successfully integrated into the CRM domain, from sales and marketing to customer assistance and retention. Computer Vision can be a force multiplier in retail, providing valuable insights into customer behavior and aiding both upselling and cross selling. It can add essential information to a customer's profile based on visual data from smart telematic devices, a game-changer for insurance and utility companies. It can also help predict issues before they happen, allowing customer care teams to avoid dissatisfaction and churn. When a customer reaches out to a company with a technical or service issue, Computer Vision can effectively route the case to the relevant agent, and help the employee diagnose and resolve the problem much faster than if they were relying on voice or text alone.
According to MIT neuroscientist Mriganka Sur, half of the human brain is devoted to vision. I'm no neuroscientist, but I imagine that there are two reasons: First, vision is extremely valuable: humans use it constantly, and for an endless variety of tasks, from reading to navigation to creating all manner of objects. Second, vision is a hard problem, considering all of the things that we're able to discern visually--under widely varying and often very challenging conditions, such as glare and low light. Computer vision enables machines to understand things through visual inputs, sometimes even exceeding the capabilities of human vision. For decades, computer vision has been a niche technology, because computer vision equipment has been large, expensive, and complex to use.