Skeltrack: A Free Software library for skeleton tracking (LinuxTag 2012)

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Information about Skeltrack: A Free Software library for skeleton tracking (LinuxTag 2012)

Published on February 27, 2014

Author: igalia



By Joaquim Rocha.

With the release of the Kinect device, there was finally an affordable camera capable of giving depth information. This, together with the Kinect's open USB connection, led to a lot of innovative projects.

Still, the Kinect just gives raw signals and the only way to obtain more complex information, such as skeleton tracking was to use either the Microsoft SDK or the OpenNI framework. Both of these solutions are closed, proprietary and, in the case of Microsoft's, only for non-commercial work.

To solve the issue above, Igalia developed Skeltrack, a Free and Open Source library published under LGPL that performs human skeleton tracking and identifies a number of skeleton joints. It is a more atomic solution than the other commercial counterparts because it does not connect directly to the Kinect nor to any other depth camera, instead it expects to be given the buffer corresponding to the depth buffer.

In this talk I will present how Skeltrack was developed and show a demo of it working.

Skeltrack - Open Source Skeleton Tracking Joaquim Rocha, Igalia LinuxTag 2012 - Wunderbare Berlin

Guten Tag! ✩ I am a developer at Igalia ✩ I like doing innovative stuff like OCRFeeder and SeriesFinale ✩ and today I am presenting my latest project: Skeltrack

The Kinect

Microsoft's Kinect was the first camera with a price affordable to the public

The USB connection is open and thus hackable

This originated Open Source projects like the libfreenect, a library to control the Kinect device and get its information

We created a GLib wrapper for libfreenect called GFreenect

GFreenect offers asynchronous functions (and some synchronous as well) and makes it easy to use with other GNOME technologies

GObject Introspection = free bindings (Python, Javascript, Vala)

Kinect has a structured light camera which gives depth information

But that's raw information... values from 0-2048

libfreenect/GFreenect can give those values in mm


It does NOT tell you there is a person in the picture

Or a cow

Or an ampelmann

Let alone a skeleton and where its joints are

For this you need a skeleton tracking solution

Three proprietary/closed solutions exist:

Microsoft Kinect SDK: non-commercial only

OpenNI: commercial compatible

Kinect for Windows: commercial use allowed but incompatible with the XBox's Kinect

Conclusion: There were no Free solutions to perform skeleton tracking... :(

So Igalia built one!

Enter Skeltrack

What we wanted: ✩ A shared library, no fancy SDK ✩ Device independent ✩ No pattern matching, no databases ✩ Easy to use (everybody wants that!)

Not as easy as it sounds!

After some investigation we found Andreas Baak's paper "A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera"

However this paper uses a database of poses to get what the user is doing

So we based only part of our work on it

How does it work?

First we need to find the extremas

Make a graph whose nodes are the depth pixels

Connect two nodes if the distance is less than a certain value

Connect the different graph's components by using connected-component labeling

Choose a starting point and calculate Dijkstra to each point of the graph; choose the furthest point. There you got your extrema!

Then create an edge between the starting point and the current extrema point with 0 cost and repeat the same process now using the current extrema as a starting point.

This comes from Baak's paper and the difference starts here: choosing the starting point

Baak chooses a centroid as the starting point We choose the bottom-most point starting from the centroid (this showed better results for the upper body extremas)

So we got ourselves some extremas! What to do with them?

What extrema is a hand, a head, a shoulder?

For that we use educated guesses...

We calculate 3 extremas

Then we check each of them hoping they are the head


For each extrema we look for the points in places where the shoulders should be, checking their distances between the extrema and between each other.

If they obey those rules then we assume they are the head'n'shoulders (tm)

With the remaining 2 extremas, we will try to see if they are elbows or hands

How to do it?

Calculate Dijkstra from the shoulders to each extrema

The closest extrema to any of the shoulders is either a hand of an elbow of that shoulder

How to check if it's a hand or an elbow?

If the distance between the extrema and the shoulder is less than a predefined value, then it is an elbow. Otherwise it is a hand.

If it is a hand, we find the elbow by choosing the first point (in the path we created with Dijkstra before) whose distance exceeds the elbow distance mentioned before

There is still some things missing...

Future work

Hands from elbows: If one of the extremas is an elbow, we need to infer where the hand is

Smoothing: Smooth the jittering of the joints

Robustness: Use restrictions to ignore objects that are not the user

Multi-user: Track more than one person at a time

And of course, get the rest of the joints: hips, knees, etc.

How to use it?

Asynchronous API

SkeltrackSkeleton *skeleton = SKELTRACK_SKELETON (skeltrack_skeleton_new ()); skeltrack_skeleton_track_joints (skeleton, depth_buffer, buffer_width, buffer_height, NULL, on_track_joints, NULL);

Synchronous API

SkeltrackJointList list; list = skeltrack_skeleton_track_joints_sync (skeleton, depth_buffer, buffer_width, buffer_height, NULL, NULL);

Skeleton Joint: ID: HEAD, LEFT_ELBOW, RIGHT_HAND, ... x: X coordinate in real world (in mm) y: Y coordinate in real world (in mm) screen_x: X coordinate in the screen (in pixels) screen_y: Y coordinate in the screen (in pixels)


Nifty Tools for Development: GFreenect: GFreenect Utils:

GFreenect Python Example

Tool: record-depth-file

Tool: depth-file-viewer


Creative Commons pictures from flickr: Kinect: Ampelmann: echiner1 Kid Playing: Rob Welsh Skeleton: Dark Botxy

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