Architecting a human face detector system
We will start by defining the business use case, its utility, and an architectural diagram of how the components work together.
The idea is to collect a video feed from where you can detect multiple objects and respond accordingly. For example, in our case, we are detecting a human face in a real-time video feed. This system could capture the feed from the front of your house and work as a security system. Or, you can apply the same workflow to detect potholes on the road through a continuous video feed collected by a car.
Once the camera captures the feed, it sends the video frame by frame to an application running on your OpenShift cluster, which then calls the model for inference. Once the model detects a face, the calling application displays and stores the results in a Redis cache (you can further enhance the application to store the results in a database), from where you can display the result or generate an alert. The backend application...
