PoolTracker DDR

This week is our final push to put together our project before the demo. The team worked to collect better footage, fine-tuning the tracking, and trying new techniques for stroke classification. We haven’t quite hit our metrics we set at the beginning of the semester, but we are pleased with our effort and results despite obstacles encountered during the semester.

At this point, the greatest risk to our project is how we present it at the demo day. We won’t be making any significant changes to the project itself, so we want to focus on how we will show our work to the outside world.

This week I spent a lot of time putting final touches on our project. One piece that needed improvement was tracking, and Adithya and I went out to get better footage so that our tracker could be more consistent.

Mostly, however, I spent time working on stroke classification and putting things together for the final demo. I have spent a good chunk of time working with the team to extract meaningful features from our video footage, as well as trial and error with machine learning models for classification.

One more day of hard work and we’ll be through the finish line!

I spent many hours this week working on finalizing the web app with Adithya. The entire process of capturing, uploading, viewing, and exporting workouts is complete with no known bugs. I feel that this part of our project looks really good!

After completing the web app, I turned my attention towards stroke classification. We chose to use separate footage from a perfectly aerial perspective for this purpose. The goal is to extract the shape of a swimmer’s body as best we can to train on that. We have currently achieved 56% accuracy.

As the final demo comes closer, we are putting in more and more work to make sure that our project is complete. We have our web app working almost completely, and our tracking functionality working fairly well.

At this point, our biggest risk factor/challenge is going to be getting stroke classification working. We have tried multiple methods for limbic detection with little success. One backup option we have is doing classification with just the raw or processed video stream. While it’s unlikely this will be as effective as classification based on limbic movement, we are hopeful that we can get some positive results.

By the middle of this week, we plan to turn our entire team’s attention towards stroke classification and polishing the tracking functionality.

This week I once again spent a lot of my time working with the team in a collaborative hack session. Big strides were made in the web app, including full capabilities added for parsing and handling incoming data. Part of this was adding the ability to read and write a workout to a CSV file, which allows us to develop the web app independently.

I plan to help make sure the web app is completely finished by the beginning of this coming week so my attention can be turned towards improving tracking and joining Karn’s efforts towards stroke classification.

This week I spent a lot of time working with Adithya to put together various components for the demo. We now have it so that the entire process from the backend to the frontend is automated, from detecting a swimmer touching or leaving a wall to their lap time being displayed on the web app.

In particular, we spent a good chunk of time adding filtering capabilities to the web app and also working on debugging the tracking software. While everything does work, the tracker could definitely use some improvement, and I imagine that this is where I will spend a good deal of time in the future.

Next week I am almost completely unavailable as I am building and operating a dark ride for carnival. If anybody is reading this, here’s my shameless plug: Follow @theoldmillride on Instagram to learn more, or stop by Skibo gym 3-9pm any day of carnival for a free ride!

This week, most of my progress has been made working with the group as a whole. Because we are putting together our demo, it’s important that we’re meeting as a team to synthesize the different components of our project.

Adithya and I worked together to make the swimmer tracking much more reliable than it was before. We are now able to do wall detection, which allows us to calculate lap times and send it over to the web app. I also worked to put together the graphing capabilities on the front end, although there is a lot of growth here from a UI standpoint.

In the next week, I’ll be focusing on polishing things up for the Wednesday demo. After that, I’ll be refocusing my attention towards another one of my classes, where I’m helping to build a dark ride for Carnival. With load-in beginning on Friday, I do not expect to have much time for capstone next weekend.

This week, our team has been putting together the different components of our project for the upcoming demo. In particular, we have focused on getting the backend to communicate with the frontend and update the web app every time a swimmer completes a lap.

Currently the biggest risk to our project is stroke classification, so we have one team member dedicated just to setting up an infrastructure for this. Even though stroke classification won’t be part of our demo, we decided to collect additional aerial footage that we believe will be more conducive to this task.