I'm quite new to machine learning (I followed the Coursera course of Andrew Ng and now starting deeplearning.ai courses).

I want to classify human actions real-time like:

  • Left-arm bended
  • Arm above shoulder
  • ...

I first did some research for pre-trained models, but I didn't find any. Because I'm still quite new, I want to have advice about how I should solve this.

  1. I thought maybe I need to create for every action enough pictures and from there on I can do image classification.

  2. Or I use PoseNet from TensorFlow so that I have the pose estimation points. And from there on I create videos of a couple of seconds with every pose I want to track and I save the estimation points. From there on, I use a classification algorithm (neural network) to classify those points.

What is the most efficient option or are they both bad and is there a better way to do this?


1 Answer 1


My suggestion is to go with 1st option. reason is you will get to know much about data and initial stage will find some challenges in developing the model, over a period of time you will get to better results after hypertunning. Please go through article , ignore you have already read this article

  • $\begingroup$ Thanks for the article will read this. Any idea how I get a lot of images or how many I actually need? $\endgroup$ Commented Dec 27, 2019 at 13:25
  • $\begingroup$ Are you looking for label data with images or just images, have found one data set, link. $\endgroup$ Commented Dec 30, 2019 at 7:11
  • $\begingroup$ Thank you. For me it doesn't matter I can label them myself. Like for example pictures of people bending their arms. When searching for this on google not a lot of pictures show up. In the database you shared per action 280 pictures exist. $\endgroup$ Commented Dec 30, 2019 at 10:41

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