Browsing by Subject "Framework (Computer program)"

Now showing 1 - 2 of 2

Open Access
Motion capture from single video sequence
(2006) Demir, İbrahim
3D human pose reconstruction is a popular research area since it can be used in various applications. Currently most of the methods work for constrained environments, where multi camera views are available and camera calibration is known, or a single camera view is available, which requires intensive user effort. However most of the currently available data do not satisfy these constraints, thus they cannot be processed by these algorithms. In this thesis a framework is proposed to reconstruct 3D pose of a human for animation from a sequence of single view video frames. The framework for pose construction starts with background estimation. Once the image background is estimated, the body silhouette is extracted by using image subtraction for each frame. Then the body silhouettes are automatically labeled by using a model-based approach. Finally, the 3D pose is constructed from the labeled human silhouette by assuming orthographic projection. The proposed approach does not require camera calibration. The proposed framework assumes that the input video has a static background and it has no significant perspective effects and the performer is in upright position.
Open Access
A multi scale motion saliency method for keyframe extraction from motion capture sequences
(2010) Halit, Cihan
Motion capture is an increasingly popular animation technique; however data acquired by motion capture can become substantial. This makes it di cult to use motion capture data in a number of applications, such as motion editing, motion understanding, automatic motion summarization, motion thumbnail generation, or motion database search and retrieval. To overcome this limitation, we propose an automatic approach to extract keyframes from a motion capture sequence. We treat the input sequence as motion curves, and obtain the most salient parts of these curves using a new proposed metric, called 'motion saliency'. We select the curves to be analyzed by a dimension reduction technique, Principal Component Analysis. We then apply frame reduction techniques to extract the most important frames as keyframes of the motion. With this approach, around 8% of the frames are selected to be keyframes for motion capture sequences. We have quanti ed our results both mathematically and through user tests.