This week, in addition to doing more validation and testing for our new 4-camera system, we were able to connect the backend database to the web application in real-time. Over the course of this week, we figured out a way to make real-time updates to the database file (with a <= 1 minute latency) while testing is in progress. We then connected the PostgreSQL database to the Django server of the web application through a Python package called Psycopg2.
Next week, we will continue to put the finishing touches on the project. We will test the prediction accuracy and try our best to exceed the 80% 1-hour prediction accuracy benchmark we set earlier in coming up with the use-case requirements. We have performed numerous tests over the course of the semester, and our entire testing log can be found here. This sheet answers the Week 10 specific question for the status report. While we would have rather hoped to be done with testing at this point, the progress of our project can be seen as “on track” as a result of many unforeseen circumstances that have happened during this semester.