PostDoc New Zealand 

During my PostDoc in Auckland, New Zealand I developed a marker-less MoCap system (click on the images for example videos). The task is to determine the 3D position, orientation and kinematic chain configuration of human beings. Therefore I dealt with four main tasks:

Develop a pose estimation algorithm for rigid 3D free-form surface models (e.g. the tea-pot). Extending the pose algorithm to kinematic chains (to model human body movements). Embed morphing approaches for more realistic models to obtain more accurate results Perform a quantitative error analysis with a commercial available marker based tracking system.

Try to click on the images to see some example videos !

Pose estimation of rigid objects

The left images show results obtained from my algorithms. I implemented an algorithm for silhouette based pose estimation. This means as input image data I assume only the boundary of the object in the image as shown left. I am able to deal wih noisy image data, as the shadows under the car or the hand grasping the tea pot. Furthermore, it is possible to handle aspect changes of the object very efficiently and the object on the turntable is a nice example that I am able to deal even with 360 degree rotations of the object. The processing time on a Linux 2GHz machine varies between 100 and 300 ms for each frame(!). See (here) for a recent publication.

Multi-view MoCap

The left figure shows a MoCap example of a stereo sequence. The figure is splitted in the left camera (top) and right camera (bottom). Each part shows the original image in the upper left, and the used corner features in the lower left. The images in the middle show pose results overlaid with the original images and the right images show pose results in a virtual environment. See (here) for a recent publication.

Morphing during Human Motion Estimation

The left figure shows an example for morphing techniques: Its left image shows the morphed joint transformed arms, and the right one the non-morphed arms. The angles of the upper arms steer the amount of morphing during lowering or raising the shoulders. Here a global morphing is applied, but I have also implemented local morphing techniques by using radial basis functions (RBFs). The left motion (see also the video) appears much more natural. See (here) for a recent publication.

Sports Movement Analysis

The left figure shows further example images taken from 4-camera sequences in the GAIT-Lab. As can be seen, we are able to track complex motion patterns to analyse e.g. push ups or sit ups.

Quantitative error analysis For a quantitative error analysis, we compared our silhouette based tracking system with the marker based Motion Analysis tracking system. We observed a deviation of less then 3 degrees. See (here) for a recent publication.