EEC-693/793 Applied Computer Vision with Depth Cameras Lecture 8 Wenbing Zhao wenbing@ieee.org

Outline Human skeleton tracking

Skeleton Tracking Real-Time Human Pose Recognition in Parts from Single Depth Images, by J. Shotton et al at Microsoft Research Cambridge & Xbox incubation http://research.microsoft.com/apps/pubs/default.aspx?id=145347 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

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

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

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

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

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

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;

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) { }

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];

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

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); }

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

GUI Design Canvas control, then add Ellipse control in Canvas

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(); }

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);

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);

EEC492/693/793 - iPhone Application Development Challenge Task 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 11/21/2018 EEC492/693/793 - iPhone Application Development