Vayum
This week I worked on developing the socket needed to transfer data from our cameras and sensors to a central server for all of our data processing. In addition, I specified the web app implementation for and began working on the MVC architecture needed. The implementation is as follows below.
In addition to this, I looked more into the process of the predictive features for determining business of the restaurant. After researching how Yelp and Google Times do it, I will be making a KNN classifier to give segments of busiest to non busiest times, with a bar graph or something similar to display the relevant information.
The next step is to start generating test data to test to see if my socket works, and how to best format my database. Most of the actual backend implementation with the front end will begin after the break ends. I am on schedule so far in everything that I am doing and I think we are progressing well.
Ajay
This week we spent most of our time working with the design review and writing the report out and getting that well specked out.
In terms of the reidentification work I did not accomplish as much I would have liked. I got the Yolo v3 tiny to work but this was not as important as a GPU instance will be plenty fast enough with the default yolo detector for our purposes. I did more reading and I think I have figured out the algorithmic approach I want to take to determine the dominant color regions for the feature vector. After taking a convolution of the image to blur together the colors, I will try solving this via a connected components approach to get the color blobs. I am also looking at the RGB/YUV histogram to extract that into a feature vector as well. Next week I want to place the orders for the Raspberry PI’s/ cameras and get a rudimentary feature extractor working.