This week, I completed some more tests on the people counter, and updated it to improve its functionality and reliability. I also spent time working on the Machine Learning model, which is now almost ready to run. In particular, I got the weather API incorporated into the data, to improve the model’s accuracy.<\/p>\n
In order to test the people counter, initial tests were done by making sure the sensor’s detect people at appropriate distances. After the counter was set up at the gym, its reliability was checked by observing how accurately it counted people walking through. A lot of edge cases were also tested and dealt with. For the ML model, a part of the dataset will be used to validate its accuracy. Furthermore, its predictions will be compared against the following week’s ground truth data in order to ensure it is predicting values correctly.<\/p>\n
For the next week, I intend to work on getting the ML model to run. I also want to work on building a system to collect occupancy data from the EC2 instance.<\/p>\n
With these updates, I am on track with our schedule.<\/p>\n","protected":false},"excerpt":{"rendered":"
This week, I completed some more tests on the people counter, and updated it to improve its functionality and reliability. I also spent time working on the Machine Learning model, which is now almost ready to run. In particular, I got the weather API incorporated into the data, to improve the model’s accuracy. In order … <\/p>\n