Hello from Team *wave* Google! Right now, the most significant risk is in the performance of the Jetson Nano. We are not sure if the Nano has the amount of computational power we need to complete both the feature extraction and the gesture recognition. If the Nano proves to be insufficient, we will need to quickly pivot to another piece of hardware, likely the Jetson TX1 or Jetson Xavier. We will try to get the Jetson Nano demo-ing as soon as possible in order to test if it has what it takes. We can do hardware testing and network training in parallel, as those two tasks don’t depend on each other. The gesture recognition demo on the Nano we saw online used OpenCV, but we want to also use OpenPose, which we are not sure if we can run on the Nano yet. This could greatly complicate our project, and the only way to mitigate is to start only.
We had to change how we implement the gesture recognition aspect of this project. We originally thought that we would’ve been able to implement our own skeletal tracking, but upon talking to the professors and reading up papers on skeletal tracking, we realized that implementing skeletal tracking from scratch would be way too hard. Thus we have two alternative approaches. The first approach is to use OpenPose and train a model with pre-processed datasets, and use skeletal tracking provided by openpose to classify gestures. The other approach is to use OpenCV and have our users possibly wear a special glove. This glove would have joints labeled, and we would use OpenCV to imitate skeletal tracking and classify gestures.
Finally, our Gantt chart and overall schedule has changed as a result of the longer than expected time for parts to arrive. This results in us not being able to completely setup the Jetson Nano and run OpenCV and OpenPose on the Nano in our initial time frame. Instead, we are forced to wait until our parts arrive, and instead run it first on the laptop. Also using OpenPose on the laptop proved more difficult than expected and would carry onto the next week.