Month: March 2019

Ajay

This week I ported my code into AWS. The biggest issue I faced was something I didn’t even expect would be an issue but when I tried to reserve a p2.xlarge instance on aws I was instantly blocked. To resolve this, i had to contact AWS support and get the instance limit to be removed for my account. After that, we were able to test our system on AWS and immediately we saw insane gains from the GPU’s. On a 6th generation i5 processor, it took around 25 seconds to do object detection using the YOLO method. With the Tesla k80, it took .15 seconds. Currently, our bottleneck is in the method we have with storing photos as it takes around 2 seconds to upload the photo to the s3 bucket. If we figure out another way of storing these photos we might approach that but I think at this point this is our best approach. I also wrote the histogram function and sketched out the function for calculating average wait time. For the average wait time, it revolves around using the total wait time so far and averaging in the next value. This week I want to work on the matching functionality. I thought we would get to it this week but the AWS setup ended up taking longer than expected.

Vayum

I finished the home page dashboard, the layout for the additional page after that and connected the web service. We also sketched up the API for how this will interact with everything else, including the YOLO detection system and the hardware sensors. Currently, I still have to write the algorithms and get more data in our database, with actual data connected from our PIs and our sensors. We also ordered parts so are we basically ready to put everything together. I would say we are on track.

 

Team Report

In terms of team status we are a little bit behind since the AWS setup took so much time. I think that we can recover the time although because now that we are set up on AWS, testing the system can happen in much faster time. Other than that our diagram for connecting our individual parts has been sketched out and the internetworks has been designed. We need to build it this week and test it.

Ajay

We missed the last status report but will update on what we’ve done overall. I’ve gotten the function that takes the photo -> recognized a person and spits out the bounding box to work. This was a simple application of the Yolo algorithm. After that, I wrote the histogram function to extract out color data from the image.  Currently, I’m running this on a CPU so my main tasks for this week are getting this running on an AWS instance. This week I also want to write the matching functionality to start getting matches from photos.

Vayum

I’ve made good progress overall on the web application portion of the project. I am currently working on the views and controller parts of the backend to deal with dynamically changing data. I’ve set up the database with all the relevant fields, have the front page working with our basic set up, and wrote the server code. This week, the plan is to finish the controller and try to get the additional pages working. I have been stuck on some bugs in the backend side of the project so hopefully I will resolve that within the next few days. Apart from that, I will soon need to start integrating with Ajay and Peter to ensure that the transfer of data from our hardware portions of the project go smoothly.

 

 

Team Report

Overall we are on schedule and are working well together. We have placed our orders for our parts and they should be arriving within the next week. There are no major risks at this point but we are monitoring the speed of the algorithms as that is the main point that we are a little worried about. Overall no schedule changes.

Attached are our design proposal slides

 

Design Proposal

 

~Ajay

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.