1. EEC-693/793 Applied Computer Vision with Depth Cameras
Lecture 8
Wenbing Zhao

2. Outline
Human skeleton tracking

3. Skeleton Tracking
Real-Time Human Pose Recognition in Parts from Single Depth Images, by J. Shotton et al at Microsoft Research Cambridge & Xbox incubation
Real-time human pose recognition is difficult and challenging because of the different body poses, sizes, dresses, heights and so on
Kinect uses a rendering pipeline where it matches the incoming data (raw depth data from Kinect) with sample trained data
The machine learned data is collected from the base characters with different types of poses, hair types, and clothing, and in different rotations and views
The machine learned data is labeled with individual body parts and matched with the incoming depth data to identify which part of the body it belongs to
The rendering pipeline processes the data in several steps to track human body parts from depth data

4. The Rendering Pipeline Processes
From depth image, we can easily identify the human body object
In the absence of any other logic, the sensor will not know if this is a human body or something else
To start recognizing a human body, we match each individual pixel of incoming depth data with the data the machine has learned
The data each individual machine has learned is labeled and has some associated values to match with incoming data
matching is based on the probability that the incoming data matches with the data the machine has learned

5. The Rendering Pipeline Processes
The next step is to label the body parts by creating segments
Kinect uses a trained tree structure (known as a decision tree) to match the data for a specific type of human body
Eventually, every single pixel data passes through this tree to match with body parts
Once the different body parts are identified, the sensor positions the joint points with the highest probable matched data
With identified joint points and the movement of those joints, the sensor can track the movement of the complete body

6. The Rendering Pipeline Processes
The joint positions are measured by three coordinates (x,y,z)
x and y define the position of the joint
z represents the distance from the sensor
To get the proper coordinates, the sensor calculates the three views of the same image: front, left, and top views => define 3D body proposal

7. Skeleton Tracking
The Kinect for Windows SDK provides us with a set of APIs that allow easy access to the skeleton joints
The SDK supports the tracking of up to 20 joint points
Tracking state: Tracked, Not Tracked, or Position Only
Tracking modes: default and seated
Default mode: detects the user based on the distance of the subject from the background
Seated mode: uses movement to detect the user and distinguish him or her from the background, such as a couch or chair

8. Skeleton Tracking Kinect can fully track up to two users
It can detect up to 6 users (4 of them with position only)

9. Skeleton Tracking Seated skeleton: up to 10 joints
The seated pipeline provides a different segmentation mask than the default pipeline:
Continuity of the segmentation mask is not guaranteed outside of the arms, head, and shoulder areas
The seated segmentation mask doesn't correspond exactly to the player outline like the standing (full-body) mask does
The seated pipeline environment has less data, with more noise and variability than the standing environment
The seated mode uses more resources than the default pipeline and yields a lower throughput (in frames per second) on the same scene
kinect.SkeletonStream.TrackingMode = SkeletonTrackingMode.Seated;

10. Capturing and Processing Sekelton Data
Enable the skeleton stream channel with the type of depth image format
Attach the event handler to the skeleton stream channel
Process the incoming skeleton frames
Render a joint on UI
this.sensor = KinectSensor.KinectSensors[0];
this.sensor.SkeletonStream.Enable();
this.sensor.SkeletonFrameReady += skeletonFrameReady;
void skeletonFrameReady(object sender, SkeletonFrameReadyEventArgs e) { }

11. Processing Skeleton Data
void skeletonFrameReady(object sender, SkeletonFrameReadyEventArgs e) {
using (SkeletonFrame skeletonFrame = e.OpenSkeletonFrame()) {
if (skeletonFrame == null) {
return; }
skeletonFrame.CopySkeletonDataTo(totalSkeleton);
Skeleton firstSkeleton = (from trackskeleton in totalSkeleton
where trackskeleton.TrackingState == SkeletonTrackingState.Tracked
select trackskeleton).FirstOrDefault();
if (firstSkeleton == null) {
if (firstSkeleton.Joints[JointType.HandRight].TrackingState ==
JointTrackingState.Tracked) {
this.MapJointsWithUIElement(firstSkeleton);
Skeleton[] totalSkeleton = new Skeleton[6];

12. Render the Right-Hand Joint on UI
We have to map the coordinate from the skeleton space to regular image space

13. Render the Right-Hand Joint on UI
private void MapJointsWithUIElement(Skeleton skeleton) {
Point mappedPoint = ScalePosition(skeleton.Joints[JointType.HandRight].Position);
Canvas.SetLeft(righthand, mappedPoint.X);
Canvas.SetTop(righthand, mappedPoint.Y); }
depthPoint will return the X and Y points corresponding to the skeleton joint point
private Point ScalePosition(SkeletonPoint skeletonPoint) {
DepthImagePoint depthPoint = this.sensor.CoordinateMapper.
MapSkeletonPointToDepthPoint(skeletonPoint, DepthImageFormat.
Resolution640x480Fps30);
return new Point(depthPoint.X, depthPoint.Y); }

14. Build TrackingHand App
Create a new C# WPF project with name TrackingHand
Add Microsoft.Kinect reference
Design GUI
Added WindowLoaded() method in xaml file
Adding code

15. GUI Design
Canvas control, then add Ellipse control in Canvas

16. Adding Code Add member variables:
WindowLoade method (WindowClosing() same as before):
KinectSensor sensor;
Skeleton[] totalSkeleton = new Skeleton[6];
private void WindowLoaded(object sender, RoutedEventArgs e) {
this.sensor = KinectSensor.KinectSensors[0];
this.sensor.SkeletonStream.TrackingMode = SkeletonTrackingMode.Seated;
this.sensor.SkeletonStream.Enable();
this.sensor.SkeletonFrameReady += skeletonFrameReady;
// start the sensor.
this.sensor.Start(); }

17. Adding Code Event handler for skeleton frames:
void skeletonFrameReady(object sender, SkeletonFrameReadyEventArgs e) {
using (SkeletonFrame skeletonFrame = e.OpenSkeletonFrame()) {
if (skeletonFrame == null) {
return; }
skeletonFrame.CopySkeletonDataTo(totalSkeleton);
Skeleton firstSkeleton = (from trackskeleton in totalSkeleton
where trackskeleton.TrackingState == SkeletonTrackingState.Tracked
select trackskeleton).FirstOrDefault();
if (firstSkeleton == null) {
if (firstSkeleton.Joints[JointType.HandRight].TrackingState ==
JointTrackingState.Tracked) {
this.MapJointsWithUIElement(firstSkeleton);

18. Adding Code For UI display
private void MapJointsWithUIElement(Skeleton skeleton) {
Point mappedPoint = ScalePosition(skeleton.Joints[JointType.HandRight].Position);
Canvas.SetLeft(righthand, mappedPoint.X);
Canvas.SetTop(righthand, mappedPoint.Y);
//this.textBox1.Text = "x="+mappedPoint.X+", y="+mappedPoint.Y; }
private Point ScalePosition(SkeletonPoint skeletonPoint) {
DepthImagePoint depthPoint = this.sensor.CoordinateMapper.
MapSkeletonPointToDepthPoint(skeletonPoint, DepthImageFormat.
Resolution640x480Fps30);
return new Point(depthPoint.X, depthPoint.Y);

19. EEC492/693/793 - iPhone Application Development
Challenge Task
For advanced students, please modify the project to make it a drawing app
Shows all traces of the hand movement
Add a virtual button to clear traces to make a new drawing
Add a small palette chooser for change the color of the drawing point (an Ellipse)
Note that you must add code such that the button/palette is pushed/selected using a gesture
4/16/2019
EEC492/693/793 - iPhone Application Development