Quite some time ago now a good friend and fellow squash player shared an idea he had with me, to create a system that is able to track a squash ball during a game and project realtime information onto the back wall.

Its an idea that really resonated with me and I have been thinking about it ever since. I want to share my thoughts here and see if anyone else has ideas on how to tackle this thorny problem.



# Prior Art

To set the stage, checkout this demo of an already existing system from [Interactive SQUASH](https://interactivesquash.com/):

<iframe width="853" height="480" src="https://www.youtube.com/embed/0N6OSVwGiRE" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

Their system tracks the ball and players using infrared sensors and cameras placed in both the tin (bottom front of the court) and in a box on the ceiling. The box on the ceiling also contains the projector which is used to update the display on the back wall in realtime.

[![](./interactiveSQUASH-Court.jpg)](./interactiveSQUASH-Court.jpg)

Its a very cool system and is the gold standard for the kind of system we would like to build. There are however a few small issues:

1. Cost. This is NOT a cheap system. They dont list their price on the website and I have requested a quote but I have been told that it is at a minimum $50k USD per court plus a yearly $5k USD subscription.

2. As explained in their [FAQ](https://interactivesquash.com/operator/) you must prepare the court for the system which could cost up to another $5k USD to paint the wall with special paint, install specific electrical cables etc.

3. It requires modifying the court. This might be a problem for a club (such as ours) that uses a shared community facility.

4. Its permanent. All the equipment must remain on site and cannot be taken home at the end of the day. This might pose a security issue when in a shared community facility.

So for these reasons we felt that there must be way we could take some of the best bits of this system but simplify things a bit and remove some of the bits we maybe dont need.

For the rest of this post im going to discuss my thoughts on this.

# Simplified Requirements

Before we can talk about solutions I want to make it clear what the requirements are.

1. At a very basic level all we want to do is know where on the back wall a ball strikes and then use that location to update a realtime projection of some scene onto the back wall. We dont really need to track the players or the ball in the air unless its comes for free as a result of the solution.

2. We want the system to be relatively inexpensive. We are not a rich club.

3. Preferably the system is portable and quick and easy to setup without any court modifications required.

# Problem Space

For those that are not familiar with the sport here is some basic info to help illustrate some of issues.

[![](./acourt.jpg)](./acourt.jpg)

A standard squash court is 9.75m in length and 6.4m in width. They can sometimes have one or more walls made of glass so you can see inside. In our case we have just the back-wall made of glass (see the above image for an example).

[![](./ball.jpg)](./ball.jpg)

A standard squash ball is about 4cm in diameter and can travel EXTREMELY fast. Here is a video of one being hit at 281km/h:

<iframe width="853" height="480" src="https://www.youtube.com/embed/4zXWu22ZhZY" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

So tracking this ball with a standard video camera is a tough problem due to its size and speed of motion.

The sport involves two players hitting the ball with a racket against the back wall and then returning it. A player wins a point if their opponent is unable to return the shot.

# Potential Solutions

Okay with the requirements listed and the problem space explained lets discuss some potential solutions.

I have split solution into two parts; first we need to detect the impact then we need a way of displaying the results on the back wall in real time.

## Impact Detection

### Computer Vision

This was my first thought. If we could mount a camera on a tripod behind the court we could potentially use some clever CV or ML based methods to detect the moving ball and track it in 3D space as it moves around the court.

<iframe width="853" height="480" src="https://www.youtube.com/embed/h_MZg6qZc8I" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

The above video uses a program called [Kinovea](https://www.kinovea.org/) to retroactively track the ball using computer vision techniques in a semi-automated manner. Its cool but we need it to be realtime and totally automated.

[This paper](https://repository.tudelft.nl/islandora/object/uuid:758d345d-ecdf-478e-a534-a23300dbe877/datastream/OBJ/download) entitled "Detection and Tracking of a Fast-Moving Object in Squash using a Low-Cost Approach" outlines in great detail the problems and techniques involved with tracking a squash ball using cameras.

[![](./cv-paper.png)](./cv-paper.png)

Their approach however has a number caveats and critically is unable to run in realtime.

The first paragraph of the conclusion of this paper sums up the problem with CV based approaches perfectly:

> Ball-tracking in sports is a difficult problem because of the small size of the ball
> and the high speed at which it moves. The varying illumination conditions, the
> shape of the ball and constant occlusion due to the continuous movement of the
> players makes it even more difficult to track the ball accurately.

It obviously is possible to solve the problem using Computer Vision as [Interactive SQUASH](https://interactivesquash.com/) demonstrates but is is a thorny problem that requires multiple cameras and sensors from different angles.

If I had spare cash I would love to try out one or two of the [OptiTrack](https://optitrack.com/) cameras which are designed for motion tracking and have some high FPS options. These cameras are used by the excellent [Stuff Made Here](https://www.youtube.com/c/StuffMadeHere) in [this video](https://www.youtube.com/watch?v=1MkrNVic7pw) and [this one](https://www.youtube.com/watch?v=myO8fxhDRW0). I am however not sure on the limits of the cameras and if they could track something as small and fast as a squash ball however.

### Audio

Another potential solution is via audio "triangulation". Using 2 or more microphones placed at suitable distances apart it should be possible to detect the sound induced by the impact of the ball on the back wall.

[![](./audio-triangulation.png)](./audio-triangulation.png)

The benefit of this approach over computer vision is that it can handle very high impact velocities as the sample rate is generally much higher. This means we should be able to analyze the details of the sound produced in greater detail even when traveling at a high speed.

The paper "[Audio-based performance evaluation of squash players](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5868801/)" uses this precise method.

[![](./audio-based-performance.png)](./audio-based-performance.png)

The authors of this paper use 6 microphones spread around the court to triangulate audio events in 3D. This allows them to detect not just wall impacts but ball-racket, player-floor and other impacts.

For our purposes however we are only interested in ball-wall impacts which is a 2D location and so dont require as many microphones to locate it in 3D space.

There are some significant issues with this solution however:

1. Distinguishing between an actual impact and an impact sound reflected from a wall.

2. Level of accuracy required by just two microphones may be too high given their short maximum separation distance and the speed of sound vs speed of light.

3. Slow impacts might be too quiet to hear or may overlap with other sounds.

4. Impacts from nearby courts might be difficult to disambiguate from our court.

### Lidar

This solution is the one favoured by my friend. If you are able to place a lidar on the floor at the base of the court and point it upwards in 180 arc then you might be able to use that to detect a ball that passes through the beam before it strikes the wall.

[![](./lidar.png)](./lidar.png)

Given you know the mirror position of the spinning laser and the distance from the beam source you can calculate where the ball is.

My main concern with this solution however is that the ball may be traveling too fast and will have struck the wall and rebounded before the laser has swept over it.

Another slight concern is how to power the lidar at the front of the court. If its power requirements are low then a battery may be feasible.

### Seismic

An interesting method, related to the audio based solution, would be to place two microphones or seismometers at the far lower corners of the court so they are both touching the back wall. You could "triangulate" the location of the impact by measuring the difference in the times that the impact event was recorded.

This solution also suffers from many of the same issues that the audio based solution as it is effectively the same solution just detecting vibration through the wall rather than through the air.

## Realtime Display

Supposing one of the above solutions is able to accurately detect an impact point on the wall we now need to update some sort of display on the back wall.

### Projector

Squash courts tend to be quite bright places this makes projecting an image on a back wall a difficult thing to do as it requires an exceedingly high-lumen projector. Even the Interactive SQUASH projector which is undoubtedly very expensive doesnt seem to produce a particularly bright image:

[![](./brightness.png)](./brightness.png)

This problem is compounded by the fact that we want the system to be portable and thus not require it to be mounted from the ceiling, this means putting it on a stand outside the court. This unfortunately means that we are going to end up with silhouettes as a player occludes the projector.

Potentially a short throw projector could be used right at the front of the court but im not exactly sure what the closest distance from the back wall you could make a short-throw projector

### Laser

An alternative method is to use a laser such as the one from the [Laser Cube](https://www.laseros.com/) guys:

<iframe width="853" height="480" src="https://www.youtube.com/embed/8KxbNctIzto" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

This solution should in theory be bright enough even in a fully lit squash court. It would however also suffer from player obstruction. It is however low power and runs off batteries so could be placed at the bottom of the court and shine upwards.

The only issue with this solution is the kinds of things you could project. You couldn't do full color images or video like you might expect from a TV or computer display but it might be sufficient for the purposes of this project.

### LED Matrix

This one is very impractical for our purposes but an alternative solution is to cover the entire back wall in arrays of LED Matrix panels effectively turning the entire back wall into a giant display. This method has actually been done before as can be seen in the following video from Interactive SQUASH

<iframe width="853" height="480" src="https://www.youtube.com/embed/LclyACKS8xs" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

This solution does however require a glass backed court (which we don't have) and an (undoubtedly) very expensive array of LED Matrix panels.

# Conclusion

So there we have it. This is something I have been thinking about for a while.

Let me know what you think, have I missed something obvious? How would you solve this problem? Let me know in the comments!

Back in in the before-times of May 2020 OpenAI made a [stunning announcement](https://openai.com/blog/tags/announcements/) about the capabilities of their latest project named GPT3. When I saw [what it was capable of](https://beta.openai.com/) and it was going to be turned into an API I knew I was going to have to tinker with it!



# TL;DR

I tried out the worlds most advanced Natural Language Processing AI Model on a real world problem and received mixed results.

# GPT3

Generative Pre-trained Transformer 3 (GPT-3) GPT3 is a MASSIVE (175 billion parameter) Natural Langue model that was trained on basically the entire internet. This means that given any chunk of text its able to comprehend it and generate a realistic output.

Seriously, if you haven't seen some of the things this AI can do stop reading this now and head over to https://beta.openai.com/examples and check them out, its pretty mind blowing.

[![](./examples1.png)](./examples1.png)

The power and possibilities of this tech are endless..

.. that is if it works as well as the examples indicate _portentous music plays_.

Eager to try it out for myself, I signed up for the closed beta on the spot.

# Many Months Later

I finally received my invite to try it out. My first step was to play with some of the examples in their playground.

<iframe width="853" height="480" src="https://www.youtube.com/embed/iumG64XEBik" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

So far so good, looks awesome!

Then I decided to set it a little challenge. One of the examples is called "TL;DR summarization":

> This prompt summarizes text by adding a 'tl;dr:' to the end of a text passage. It shows that the API understands how to perform a number of tasks with no instructions.

So given this post from my blog: [Introducing BattleTabs](https://mikecann.blog/posts/introducing-battletabs), I wonder what kind of tl;dr it will give me..

[![](./tldr1.png)](./tldr1.png)

Ah that doesn't look right. Perhaps its struggling because I gave it Markdown rather than raw text?

I decided to remove all the Markdown markup and run it again..

[![](./tldr2.png)](./tldr2.png)

Awesome! It now works and gives me a really nice summary :)

# Whats Next?

I wondered what real-world problem I could give it that would be simple but interesting enough to test its capabilities a little more.

I played with the examples a bit more while I pondered.

[One caught my eye](https://beta.openai.com/examples/default-keywords):

> Extract keywords from a block of text. At a lower temperature it picks keywords from the text. At a higher temperature it will generate related keywords which can be helpful for creating search indexes.

Well, this blog has over 600 posts on it and many of those posts are old and are missing Tags. Could I run all my tagless posts through GPT-3 and use this keyword extractor to generate the tags, I wondered.

It might be worth a shot, it would save me a bunch of time manually going through them and improve the navigability of this blog in the process.

First step was to try out this keyword extractor with my manually-markdown-stripped text from before.

As "[Introducing BattleTabs](https://mikecann.blog/posts/introducing-battletabs)" is a fairly recent post it already has some manually hand-crafted tags:

```
- chrome extension
- game
- async
- typescript
- battleships
```

Lets see what GPT-3 comes up with:

[![](./keywords1.png)](./keywords1.png)

Wow! Impressive, sure its not quite as good as my manually crafted tags but still it worked really well.

Confident with my little experiment I set to work writing a script that would run through all my tagless posts strip out the text from the markdown and call the OpenAI API to get the keywords.

Simples! ...

It turns out that stripping the markdown markup from text is actually more complex that I thought it would be. To cut a long story short I decided to render the Markdown to HTML then using Puppeteer I extract the text from the page using `element.textContent`.

I will spare you all the other steps but if you are interested you can checkout the final script [HERE](https://github.com/mikecann/mikecann.blog/blob/master/scripts/openAIKeywords.ts).

# Results

I ran the script and 623 seconds later it had run through the 213 tag-less posts from a total of 612 posts.

_If you are interested, I have uploaded all the results [HERE](https://gist.github.com/mikecann/6efd37e58c5b0a38e0182bf40ac6a71e)._

It was able to generate tags for 177 of those 213 posts leaving 39 left over that it couldnt find keywords for. This isnt too bad, I was expecting a bunch of the older posts to be too short or to reference the visual (image or video) content too much to be able to extract any meaningful keywords from the text.

But what about the ones it could find keywords for?

Well firstly the good news, it seems like it was able to find good keywords for some of them. For example [my post titled "Village Life" from back in 2012](https://mikecann.blog/posts/village-life) it was able to generate these tags:

```
"mobile",
"game",
"facebook",
"multiplayer",
"flash",
"social",
"facebook",
"multiplayer",
"flash",
"social",
"facebook",
"multiplayer",
"flash",
"social",
"facebook",
"multiplayer",
"flash",
"social",
"facebook",
"multiplayer",
"flash",
"social",
"facebook",
"multiplayer,"
```

If you take the first 6 tags `mobile, game, facebook, multiplayer, flash` which is pretty impressive given the entire of the blog post text is:

```
Village life is a flash-based social game I worked on while at Playdemic. Its incredibly popular and has been played by millions of people.

I was responsible for the underlying client side technology that was used for developing the game.

Play it on facebook: https://apps.facebook.com/villagelifegame
```

Notice that nowhere in the post do I mention the word "multiplayer" or "social" but somehow it was able recognize that the game is that its "multiplayer" and "social" too.

I suspect it knows this from the various news stories about that game around the web that it gobbled up during its training. This would also explain the "mobile" tag and segway us into the negatives results of this experiment.

The problem with the "mobile" tag is that even tho the game was eventually ported to mobile I wasn't involved with the port and at the time the blog post was written the port didn't exist for mobile.

So this is a bit of a problem if I want to use these automatically generated tags for my posts as im now labelling my posts with falsehoods.

The issues continue. You many have noticed that the tags it generated are repeated many times. Well this seems to occur more often than not e.g.

```
 {
    "title": "Started Work at TheBroth in Perth!",
    "prompt": "Blog Post: Just a quick update. After a year of living down in Bunbury Western Australia and working Freelance and on my own projects (see this blog for more) I have decided to re-enter the workforce. Mainly because I was going a little stir crazy being by myself most of the day every day.\n\n\nI have taken a job 2 hours north in Perth at a company called TheBroth. We mainly work on a mobile and web game called SlotsGalaxy. Its similar sort of work to which I was doing at Playdemic so hopefully my old skills arent too rusty :)\nIm going to continue to work on my projects such as Mr Nibbles Forever and Post To Tumblr and my (still) unreleased strategy game but obviously time is going to be a little more limited.\nI have enjoyed the past year mostly but I think it was time for a change and im looking forward to the next year!\n\nKeywords: ",
    "tags": [
      "games",
      "game design",
      "game development blog",
      "game design blog",
      "games blog",
      "game development blog",
      "game design blog",
      "games blog",
      "game development blog",
      "game design blog",
      "games blog",
      "game development blog",
      "game design blog",
      "games"
    ]
  }
```

or worse it just gives up and starts repeating the same keyword:

```
{
    "title": "Supreme Commander Wallpaper",
    "prompt": "Blog Post: Well i have just finished making a massive (4608x2048) duel-screen wallpaper for a thread on the supreme commander forums.\n(click for full screen)\n\nKeywords: ",
    "tags": [
      "for alliance",
      "for allianc",
      "for allianc",
      "for alliance",
      "for allianc",
      "for allianc",
      "for allianc",
      "for allianc",
      "for"
    ]
  },
```

This could obviously be mitigated by removing duplicates but its worrying as the even the de-duplicated tags arent really that useful.

For example for the above Supreme Commander post the de-duplicated tags would be: `for alliance, for allianc, for` which doesnt actually say anything about the post.

# Whats Going On?

Why does the result of that original experiment on the "Introducing BattleTabs" post come out so good but when I run my script many of the results come out garbage?

I decided to run a quick text, take the Supreme Commander post and manually put it into the OpenAI Playground and see what I get:

[![](./supcom1.png)](./supcom1.png)

Well thats not perfect, I would expect it to mention something about wallpaper or perhaps creation, Photoshop or something but at least its not repeating and seems to be more relevant to the game.

# Conclusion

Unfortunately I have run out of time to tinker on this little side-quest at the moment.

My conclusion is that although very impressive, the purpose I used the model for is not quite what I was looking for so in the end I decided not to use the generated tags.

Im 100% sure that this is the future tho. I mentioned [back in 2017](https://mikecann.blog/posts/a-game-developer-learns-machine-learning-intent) that:

> I see a future where an AI is capable of generating an entire game based on your preferences such as "I would like a game that plays like Mario but has the same size and scope as World of Warcraft", the AI then generates variants on that for you to play.

GPT-3 is definitely leading the way towards that being a possibility. If you dont believe me checkout [this tweet](https://twitter.com/sharifshameem/status/1282676454690451457?s=20) of someone using GPT-3 to generate HTML from some natural language.

So, although for my example I don't think its quite there, I can definitely see this becoming more and more a part of our lives.

I see programming changing drastically over the coming years, going from less of telling the computer how to do something and more having a conversation with the computer and asking it to iterate on what it generated. If you want a glimpse at what this awesome future may be like then checkout the incredible [GitHub Copilot](https://copilot.github.com/).

P.S. The generated keywords for this post are: `AI, NLP, NLU, Natural Language Understanding, Machine Learning` :)