Sometimes the business logic of a software product does not depend on the raw user data. The architecture of the software can be changed to convert users raw data into other data formats that can be used and stored for future use.
In Real Time Video based Computer vision applications the videos can be converted into numerical data and data can be stored in a database. In the following python code snippet the landmarks detection of a hand movement is explained.
This python code snippet can detect the real time hand movements and it will display the skeleton of the hand as follows.
The print statement in line no 19 will print out the real time numerical data in the console and it is listed below.
This data can be converted into a single json or if it is too large it can be converted into a string and it can be sent to the database by using the backend APIs of the application.
This architecture can be used in almost every field that uses real time computer vision like Autonomous vehicles, Manufacturing and Quality Control, Retail and E-commerce, Agriculture, Security and Surveillance, Healthcare, Robotics and Sports Analytics. With the collected data, it becomes possible to generate skeleton videos and even perform comparisons between two or more user videos, making this process incredibly adaptable and powerful.
By following this architecture, software providers can guarantee that the software will not collect the user videos and it will only generate a numerical object that contains the landmarks of the user video and since this process can be done in mobile or web applications the videos will not be saved in any other places like cloud storage.