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?


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? $\endgroup$ Dec 27 '19 at 13:25
  • $\begingroup$ Are you looking for label data with images or just images, have found one data set, link. $\endgroup$ Dec 30 '19 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. $\endgroup$ Dec 30 '19 at 10:41

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