In the paper the application of model based markerless motion capture technology to general environment and
quadrupeds is presented. Some of the authors' recent results are discussed together with the open challenges related to
the capture of animal motion. Despite its very recent history, markerless motion capture represents already both a
valuable alternative to marker based approaches and in some circumstances the only valuable solution. One of these
cases is animal capture where the positioning of markers on the animal is very challenging when possible at all. An
example of markerless tracking of animal motion is shown together with a virtual validation to provide quantitative
evidence of the robustness and accuracy of the presented method.
Modern biomechanical and clinical applications require the accurate capture of normal and pathological human movement without the artifacts associated with standard marker-based motion capture techniques such as soft tissue artifacts and the risk of artificial stimulus of taped-on or strapped-on markers. In this study, the need for new markerless human motion capture methods is discussed in view of biomechanical applications. Three different approaches for estimating human movement from multiple image sequences were explored. The first two approaches tracked a 3D articulated model in 3D representations constructed from the image sequences, while the third approach tracked a 3D articulated model in multiple 2D image planes. The three methods are systematically evaluated and results for real data are presented. The role of choosing appropriate technical equipment and algorithms for accurate markerless motion
capture is critical. The implementation of this new methodology offers the promise for simple, time-efficient, and potentially more meaningful assessments of human movement in research and clinical practice.
The most common methods for accurate capture of three-dimensional human motion require a laboratory environment and the attachment of markers or fixtures to the body segments. These laboratory conditions can cause unknown experimental artifacts. Thus our understanding of normal and pathological human movement would be enhanced by a method that allows capture of human movement without the constraint of markers or fixtures placed on the body. Markerless methods are not widely available because the accurate capture of human movement without markers is technically challenging. A reported method of constructing a body's visual hull using shape-from-silhouette (SFS) offers an attractive approach. However, to date the influence of camera placement and number of cameras on construction of visual hulls for biomechanical analysis is largely unknown. The purpose of this study was to evaluate the accuracy of SFS construction of a human form for biomechanical analysis dependent on camera placement and number of cameras. Visual hull construction was sensitive to camera placement and the subject's pose. Uniform camera distributions such as circular and hemispherical camera arrangements provided most favorable results. Setups with less than 8 cameras yielded largely inaccurate visual hull constructions and great fluctuations for different poses and positions across a viewing volume, while setups with 16 and more cameras provided good volume estimations and consistent results for different poses and positions across the viewing volume.