What did you personally accomplish this week on the project?
I continued worked on testing my pose recognition UI, and generating testing metrics to see if we meet the design requirements. I also worked on the final presentation.
Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?
Behind schedule. We still need to integrate everything together. Catherine’s code should in theory integrate with my code base. I’ve also verified that Anna’s code works with mine. So, pairwise, the systems have been integrated. But we still need to integrate everything as a final product.
What deliverables do you hope to complete in the next week?
Integration. We hope to have a (barely) working final product by the end of this week.
Testing:
To test my subsystem, I’ve created tests for each of the design requirements we are trying to target, (1) latency (2) input accuracy and (3) pose estimation accuracy.
- To test latency, I’ve simply measured the time it takes for the gesture recognition pipeline to receive the image and then provide pose estimates (essentially model evaluation time). This was simple as inserting “stopwatches” into the codebase, and averaging measurements. The end-to-end latency was about 55 ms, which meets our target.
- For input accuracy, I made “test inputs” for each of the inputs that we wanted to test. Since the inputs are now location based, these tests essentially consisted of holding up my hand over the button I wanted to click on the screen. Overall, this test went very well, since the buttons were fairly large and OpenPose wasn’t noisy enough to “miss” these buttons. Out of the 20 trials I did for a button, 19-20 of them would “pass” (>= 95% acc) — which means registering an input. However, the input accuracy would change under different lighting conditions, meaning that if the room was too bright or too dark, the accuracy would drop to around 18-20 / 20 successes (>= 90% acc), which is still good enough for our purposes.
- For pose estimation, I held my hand relatively stationary, and measured the standard deviation, and max deviation from the mean. For a 1080p camera, this deviation was about 35 px, which is a little more than we’d like (<20px), but is still good enough for our purposes. Note that this metric is bound by the model we choose (in this case OpenPose).