Thing-Finding With Kinect DK

Thing-Finding With Kinect DK

In this article (and the companion github repo : mpdroid/bones), we explore use of this kit with Azure Cognitive Services to enhance how a person can interact with objects around them in 3-dimensional space.

The kit provides hardware/software to capture videos in color/depth and to extract body tracking information from them. In this article (and the companion github repo : mpdroid/bones``), we explore use of this kit with Azure Cognitive Services to enhance how a person can interact with objects around them in 3-dimensional space.

What is inside the box?

The hardware device is supported by two SDKs:

  • Sensor SDK — a C API to connect to and start the device, to extract depth and color images and to transform points between depth and color coordinate systems.
  • Body tracking SDK — also a C API, that extracts information about human bodies present in the FOV. Each body frame is composed of 32 joints (eyes, nose, head, hands, feet etc.), each characterized by a position and an orientation. The API also provides a “body index map”: a data structure that tells us which depth pixels belong to which body in the video frame.

The body tracking demos

Thempdroid/bones project includes a few applications that demonstrate the dev kit capabilities. The README in the github repo provides a detailed list of instructions to download, build and run the project. While the project was developed on Ubuntu 20.04, it should work on lower Linux versions and Windows. These are the pre-requisites:

  • Gnu C Compiler(gcc 9.3.0+)
  • cmake
  • ninja-build
  • Azure Kinect Sensor and Body Tracking libraries
  • Eigen3 (For vector and matrix operations)
  • Obtain an Azure Vision subscription and then store endpoint and key in AZURE_VISION_ENDPOINT and AZURE_VISION_KEY environment variables respectively.

The project also makes use of a few external libraries that are either embedded with the source code or get downloaded when you git clone it with the — recursive option;

The diagram below shows how the code is organized.

  • Sensor API methods are wrapped inside Kinector .
  • Euclid wraps body tracking API and implements the geometry.
  • Renderor handles presenting camera frames with annotations on application window.
  • *Scene classes implement scene comprehension and annotation.

body-tracking artificial-intelligence azure-kinect-dk computer-vision

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