I researched about detecting image change and implemented an image detection algorithm using the CV2 module (open-cv library). I took a few images on the spot where each pair captures the same objects, one with hands and one without hands. I ran the script and recorded their matching error and runtime. The runtime is 0.02s, much lower than what is expected for YOLO, which is exciting. More experiments need to be run to set a reasonable threshold for declaring an “image change”. The code and sample images are linked below.
Besides that, most of the work done this week is for the design review document due next week. I have solidified the introduction, use case requirements and design requirements and written them on the design doc. I have also updated the design of the FSM by setting a timeout at the waiting state where we wait for the second, consecutive change. I am also writing about that in design trade studies as well as system implementation.
Earlier this week Ting and I met again to talk about the ML model and we are understanding the labeling and images structure better. We are planning to add to the “partition” script and partition labeling files to train, test and validation folders and run the model again. Hopefully it could run until fine tuning this time. This should be achieved before the spring break.
Image detection code & sample pictures (results logging and script instructions can be found in the code file):
https://github.com/AichenYao/capstone-scripts/tree/main/image_detection