Skip to main content

Pose recognition with Intel's Realsense and OpenVino

Just a short post today on Intel's Openvino toolkit. It is really a great collection of machine learning models that can speed up development. I played around with their pose estimation model and the results are quite good.

Final result of the pose detection system

This system combined with the Intel Realsense depth cameras offer a promising real world solution to the pose estimation problem. As shown in the video, it works reasonably well on my laptop with an NVIDIA GPU (1060) Graphics card. The white spheres indicate the feet limbs and thanks to the Realsense depth camera we can estimate the positions of the limbs in 3D space.

Although I haven't tried it yet but the models can be converted to work on edge platforms such as the Movidius device.
Obviously this opens up a whole array of applications which I hope to share again in a future post.

Comments

Popular posts from this blog

Using FCM with the new HTTP v1 API and NodeJS

When trying to send FCM notifications I found out that Google has changed their API specifications. The legacy API still works but if you want to use the latest v1 API you need to make several changes. The list of changes is listed on their site so I won't be repeating them again but I'll just mention some of the things that caused some trial and error on my project. The official guide from Google is here : Official Migration Guide to v1 . The request must have a Body with the JSON containing the message data. Most importantly it needs to have "message" field which must contain the target of the notification. Usually this is a Topic, or Device IDs. Since my previous project was using GAS, my request had a field called "payload" instead of "body". Using the request from my previous project, my request in Node JS was as follows: request ({ url: 'https://fcm.googleapis.com/v1/projects/safe-door-278108/messages:send' , method: ...

Object detection with Google Colab and Tensorflow

This is just a memo of the challenges I faced when running a model training on Google Colab, while following a great tutorial here . Mind the versions Tensorflow is currently at version 2.2.0 but most tutorials are still using the contrib package, and there is no known easy way to update the code to remove dependency on contrib. So my best bet is to downgrade the tensorflow version to 1.x. Since Google Colab only gives the options of either 1.x or 2.x and we cannot specify the exact version, I ended up with version 1.15.2. Even with the command :  %tensorflow_version  1.15.0 I ended up with : 1.15.2 Another pitfall was the version of numpy. Installing numpy gives us the version 1.18.3 but for some reason this generates the error : TypeError: 'numpy.float64' object cannot be interpreted as an integer Downgrading numpy to version 1.17.4 solved this for me. It seems we don't need ngrok for tensorboard With the command :  %load_ext tensorboard W...

Microsoft Azure Face API and Unity

During one of my projects, I came across Microsoft's face recognition API (Azure Face API) and it looked good enough to recognize people's faces and detect if a person is a newcomer or a repeating customer to our store. As our installations mainly use the game engine Unity, I wanted to be able to use the Face API from Unity. Face API does not have an SDK for Unity but their requests are just HTTP requests so the Networking classes in Unity can be wrapped into methods to make it easy to call these APIs. First of all, to those who just want to see the code, here it is . My tests focus on the identification of a face in an input image. The full tutorial I followed can be found here . The Main scene goes through the steps in the tutorial such as creating a PersonGroup and adding Persons to the group if it is not created yet. Just make sure you: Change the API key. I used a free trial key which is no longer valid. Use whatever images you want. I don't mind you us...