This week I was able to finish writing our video testing script that we are distributing to friends and family in order to gather more data. We have been beginning to test our algorithm locally using the videos in our testing suit to gain an analysis on how accurate our program is.
One of our biggest goals is to improve the performance of our algorithm on the board. During our weekly meeting with Professor Savvides, we came to the agreement that we are going to prioritize optimizing our program by making it faster and increasing our fps. This week in particular, I looked into having the OpenCV’s “Deep Neural Network” (DNN) module run with NVIDIA GPUs. We use the dnn module to get inferences on where the best face in the image is located. In the example tutorial we are following, they stated that running it on the GPU would have >211% faster inference than on the CPU. I have added the code for dnn to run on the GPU and we are currently in the process of having the dependencies fully work on the board in order to find out what our actual speed increase is which we will showcase during our demo next week.
I would say that I am currently on track with our schedule. The software optimization is my biggest task at the moment but it is looking pretty good so far! For next week, I am hoping to have some big board fps improvements in the upcoming days. This would greatly improve our algorithm and make the application more user friendly.
Source: https://www.pyimagesearch.com/2020/02/03/how-to-use-opencvs-dnn-module-with-nvidia-gpus-cuda-and-cudnn/