Category: Status Report

Last week, I finished making the model using my custom dataset. I came across a major problem when it came to working the YOLO model, which is what we had previously intended. Initially, I had thought that an object detection model like YOLO was ideal, since it is trained to pick up different objects in a scene. However, one shortcoming I did not foresee was that the images we would collect in our system were not like the natural images, because they were taken from an aerial view. The following two pictures display this disparity.

 

 

 

 

 

 

In the first image, we see the picture is taken from the side. The second image is taken from the top. For human beings, it is easier to identify the presence or absence of other humans, but for a machine learning model, this cannot be abstracted.

In order to fix this problem, I decided to use my own machine learning model, without any pretrained weights. The advantage of this is that it will solely focus on data that we have fed it, and so it doesn’t need to depend on natural images. However, the disadvantage is that I need to make a simpler model, since there is less data availability. For this reason I had to adapt the model, where instead of the model finding the location of the seats, I specify the location of the seats. Then, the algorithm would crop out each seat, resize the pictures and identify whether there was a person in each seat or not. I thought this was a fair compromise, given that the seats in Pablo’s dining room are in a relatively fixed position, and so is the camera that we set up. If we were to take this project further, there would be an extra cost associated with installing the system in a new location, but this cost would be negligible compared to the effort it would take to mount the camera and central nodes at the location.

Additionally, I worked on the frontend of the website. When we had planned on setting up in Sorrell’s Library, I did not know the layout of the seats, especially since we were not sure where we could have mounted the cameras. Once I got a few pictures of Pablo’s dining room, I could understand the layout and set up the website to mirror that layout. Right now, the website can read four occupation bits and display an appropriate html page with red and green colors based on availability, at different locations representing locations at the dining table.

This week, I worked a bit more on the machine learning model. At the review, Professor Yu had suggested I make a small dataset from a few images of my desk. While this is not ideal, it does help me start some of the work. I have faced some issues with the model which I plan on debugging on Monday and after Thanksgiving break. I have two main issues right now, namely overfitting and sensitivity to hyperparameters.

Overfitting. With a small dataset, it is easy to make a machine learning model that memorizes the specific output expected on the training data. This does not identify patterns and therefore doesn’t generalize well. Due to the delays in acquiring data, I haven’t been able to collect a dataset of the right size, so the model is not as strong as I would like it to be.

Sensitivity to hyperparameters. Despite having only a small dataset, there are issues even with learning the training data. Small changes in the training hyperparameters cause large changes to the accuracy of inference on the training dataset. This will get worse as the dataset increases in size. This is a more fundamental issue that I need to research more before I attempt to fix it. My guess is that there is an issue with running darknet on a Jupyter notebook. (Darknet is the platform on which YOLO is written). One fix that I want to explore is changing the version of YOLO to one that was written in PyTorch (i.e. Python). This might fix the issue.

I also wrote a short python script that solves the problem I described in the last status update. One issue that Pablo is facing is that some of the images lose all their data and the bottom part of the image becomes a set of vertical lines. I described the approach in the last status update, but I had not implemented it then. Since then, there have been no problems with implementing my approach.

There is still not much that I could have done this week, before we get the data from Sorell’s. However, I did manage to find some useful resources. Specifically, I found a website called Roboflow, which will allow me to take my labelled training data and run some preprocessing on them. This is different from the preprocessing that we plan on running in the central node, as it specifically pertains to the machine learning model.

The main advantage that Roboflow offers is that it will help me convert images from XML to the darknet format in bulk. For the initial picture of my workspace that I used to test the pipeline, I did this manually. Now that I have found Roboflow, I can do this automatically, saving a lot of time to process thousands of images.

Another advantage that Roboflow offers is increasing the size of my dataset. It lets me perform transformations like rotation, scaling and blurring on duplicates of the images in the dataset. With combinations of these transformations I could increase the size of my dataset by a factor of 3-10. One consideration I will need to make is that this compromises on the quality of the dataset, since the images will have some similarity among themselves.

On a different note, one issue that Pablo brought to my attention is where some of the images taken are distorted. The bottom part of the image is cut off and replaced with vertical lines, as shown in this picture. Pablo mentioned this was due to a wiring issue, but I am also planning on solving this problem in software.

One thing to note is that the lines that cause the distortion are all perfectly vertical and they are always at the bottom of the picture. This can be detected using a vertical Sobel filter. The Sobel filter is a high pass filter for images in one dimension. Since there is no change vertically in the bad part of the image, there is only a DC bias. A high pass filter will remove this bias and leave the bottom half of the image to be all zeros. After that, we simply need to compare the last bottom lines of the image to zeros in order to detect this kind of error.