Less than nine months ago, the deployment of touchscreens was accelerating. Everywhere you visited or shopped, there were new self-service touchscreen devices to help you find directions or product information, order services, or dispense food and drink. However, one of the fallouts of the COVID-19 pandemic is that consumers are now resistant to touch these public devices.
To continue providing customer self-service, the user interaction with these devices must offer a safe, touchless alternative. Voice is an obvious option, but it is problematic in noisy, public environments. Consequently, many companies are employing multiple strategies in addition to touch and voice, including facial recognition, gestures, sensors (e.g. thermal cameras and RFID readers), and innovative companion apps that use a consumer’s own smartphone as the touchscreen for a public device.
In this webinar we discuss use cases as well as strengths and weaknesses for these alternative measures, including:
The introduction of low-cost, high-performance embedded processors coupled with improvements in Neural Network model optimization lay the foundation for AI and Computer Vision at the edge. Moving intelligence from the cloud to the edge offers many advantages including the reduction of network traffic, predicable ML inference times, and data security to name a few. Challenges exist as many development teams do not have data scientist or AI development engineers. What is needed are practical AI solutions including ML development tools, optimized inference engines and reference platforms that will abstract out the development complexities to stream line prototyping and development.
In this joint webinar with Au-Zone Technologies we will discuss:
Development challenges and solutions which can be use to enable AI/ML at the edge to implement object detection, classification and tracking for medical and industrial use-cases
Visualization techniques for activity monitoring and object detection
