Achievements
This week I spent time on the fullness detection model, playing around with different implementations for detecting the fullness to see what would increase the accuracy. Right now the carts all seem to be detecting to be similar fullnesses, despite having different fullness. The current approach was using edge detection to count the contours, but since our system is capturing the carts at a relatively slow fps, the carts are also a little bit blurry, rendering the edge detection a little inaccurate. Due to this, another approach I was playing around with was observing colors, and the number of black pixels there are in the frame. This also didn’t seem to the have the best accuracy, so I am still in the process of figuring out what could be the best approach.
Progress
Since I am not really happy with the current accuracy of the fullness detection, so I will be spending the next two days to increase it before the final poster is due, after which we will be working on the final video and report. We also need to work on the final demo because all of our testing has been done at Salem’s, but once we have a 1-hour long video to display on a monitor for the demo, we also need to make sure that our system will work for our makeshift in house checkout system that we will be demoing in person in Wiegand.