Okay, here’s my take on sharing my “tennis player lajal” practice, blog-style.
Alright folks, today I’m gonna walk you through my little experiment: “tennis player lajal”. It sounds fancy, but trust me, it started from me just messing around.
Phase 1: Gathering the Ammo
- First thing I did was hit up Google Images. I needed a bunch of pictures of tennis players. Not just any pictures, mind you, I wanted a mix of different angles, different poses, you know, action shots and chillin’ on the bench shots. Think Serena Williams power serves, Federer backhands, the whole shebang.
- Next stop was YouTube. Scraped some videos of tennis matches, training sessions, anything that showed how these athletes moved. It’s key for getting the right feel.
Phase 2: The Messy Bit – Data Prep
- This was the grunt work. Took all those images and videos and started labeling them. “Forehand”, “Backhand”, “Serve”, “Running”, “Standing”. Ugh, so tedious. I swear, I almost gave up here, but I powered through. I used some free image annotation tool I found online; honestly, it was clunky, but it got the job done.
- Then I had to chop up the videos into smaller clips. Little snippets of each action. Think like, a 2-second clip of just the serve motion. This was even more tedious than labeling images.
Phase 3: Time to Build (or, at least, try to)
This is where things got interesting, and by interesting, I mean I banged my head against the wall a lot.
- I initially tried using some pre-trained pose estimation models I found on GitHub. The idea was to feed it the images and videos, and it would automatically track the joints of the tennis player. Sounds easy, right? Wrong. The models were trained on general human poses, not specifically tennis players. The results were… well, let’s just say they looked more like broken robots than athletes.
- So, I went back to the drawing board. Figured, “Okay, maybe I need to train my own model”. This meant diving into the wonderful world of transfer learning. I grabbed a model pre-trained on ImageNet (because everyone does), and then fine-tuned it on my labeled tennis data.
- Training took forever. My poor laptop sounded like it was about to take off. But eventually, after hours of tweaking parameters and yelling at my screen, I got something that was… acceptable. Still not perfect, mind you, but way better than the pre-trained stuff.
Phase 4: Putting it All Together
Okay, I had a model that could (sort of) recognize tennis poses. Now what?
- I built a simple interface using Python and some basic libraries. The idea was to upload a video, and it would spit out the predicted poses frame by frame. Think of it like a super-basic motion capture system.
- I hooked it up to my (slightly janky) pose estimation model.
- I tested it out on some new tennis videos. The results were… mixed. Sometimes it worked great, sometimes it was completely off. It definitely struggled with fast movements and blurry images.
The Takeaways:
So, what did I learn from all this?
- Building even a basic pose estimation system is way harder than it looks. Data, data, data. You need a LOT of it, and it needs to be good quality.
- Pre-trained models are a good starting point, but you’ll probably need to fine-tune them for your specific use case.
- My laptop needs an upgrade. Seriously, it was screaming.
Where to go from here?
Honestly, this was just a fun little side project. But if I were to take it further, I’d probably:
- Gather more data. Like, a lot more.
- Experiment with different model architectures.
- Try to incorporate temporal information (i.e., how the poses change over time) to improve accuracy.
But for now, I’m calling it a win. I learned a ton, and I have something that (kind of) works. Plus, I have a newfound appreciation for the people who build these things for real.
That’s the tennis player lajal story, hope you enjoyed the messy journey!
