WRITTEN COMPONENT ENTRY Low Cost Mocap notes.
- motion capture, or mocap, is a technique of digitally recording the movements of real beings, usually humans of animals.
- mocap is considered to be a better technique for accurately generating movements for computer animation.
- 3 types;
o optical motion capture
o magnetic motion capture
o electro-mechanical motion capture
- in this paper, we describe the design and implementation of a low cost motion capture system that requires two low cost calibrated webcams.
- as all mocap systems involve a tracking phase, we adopt the mean-shift algorithm as the basis of object tracking.
- system has an inability to handle occlusion.
- provides input to animation applications, such as Poser.
- the mean-shift algorithm is one of the tracking techniques commonly used in computer vision research when the motion of the object to be tracked cannot be described by a motion model.
- the key notion in the mean-shift algorithm is the definition of a multivariate density function with a kernel function ‘K’ over a region in the image.
- the commonly used kernel functions are the normal, uniform and epanechnikov kernel functions.
- amongst the commonly used kernel function above, the epanechikov kernel, which has its kernel profile as a uniform distribution, is preferable than the other two.
- the Bhattacharyya coefficient is used to calculate the similarity measure between the two distributions.
- the necessary pieces of equipment required are two low-cost webcams, two tripods and a calibration frame.
- each webcam mounted on a tripod must be calibrated prior to any experiments. The current version of the system focuses on the capture of movements of the lower part of white circular markers to be put on the following joints; hip, two upper legs, knees, ankles and feet.
- to simplify the tracking process we darken the background.
- subject wears a dark non-glossy tight suit so that the white circular markers can be easily detected.
- the two webcams are directly connected to a computer via two USB ports.
- we currently use functions under the Matlab Image Acquisition Toolbox for image acquisition.
- camera calibration is a step for determining the 3x4 matrix that maps coordinates in the 3D world into the 2D image.
- in our system, we use a calibration target with two orthogonal faces, each of which has 6 reference points. The calibration target also implicitly defines in the scene a global coordinate system that can be referenced to in some other applications, such as Poser, for graphics rendering.
- markers were detected via a thresholding process.
- involves choosing a threshold value ‘t’ from the pixel intensity range of 0 to 255.
- the threshold value can then be estimated from the flat region in the intensity value histogram.
- the 9 markers are automatically labelled using a heuristic method.
- after all 9 markers have been detected, the system labels the top middle marker as marker #1.
- the assignment of marker number on the ‘y’ component of the marker coordinates.
- the mean-shift algorithm is employed to track the 9 white markers independently.
- there are 3 free parameters that can be set to fine tune the performance of the mean shift algorithm;
o radius
o threshold value
o number of histogram bins
- for the computation of the 3D coordinates of each marker, the two 3x4 matrices obtained above are combined to give 4 linear equations for the detected image coordinates of the marker in the two images. The 3D coordinates of each marker, relative to the implicit global coordinate system defined by the calibration frame, can be estimated using least-squares.
- in every experiment, we tested our system to track the movement of markers over 200 frames.
- we found that there is a 0.016 seconds delay between the acquisition of an image by the first webcam and the second.
- we found that the radius of the kernel windows is a crucial parameter to the performance of the mean-shift algorithm.
- issue has also been reported in that a window size that is too large can cause the tracker to become more easily distracted by background clutter and a window size that is too small can cause the kernel to roam around on a likelihood plateau around the mode, leading to poor object localisation.
- system captures movement of the lower body, it can be further extended to include the upper body.
- the notion of low cost mocap is important for demonstrating the fundamental idea of motion capture and for providing inputs for various advanced animation applications.
R. Budiman, M.Bennamoun, D.Q.Huynh. “Low Cost Motion Capture”. University of Western Australia.
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