Motion Capture Implies Motion Extrapolation

Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-l-0657, N00014-95-l-0409

Measuring Behavior using Motion Capture

Motion capture systems, using optical, magnetic or mechanical sensors are now widely used to record\ud human motion. Motion capture provides us with precise measurements of human motion at a very high\ud recording frequency and accuracy, resulting in a massive amount of movement data on several joints of the\ud body or markers of the face. But how do we make sure that we record the right things? And how can we\ud correctly interpret the recorded data?\ud In this multi-disciplinary symposium, speakers from the field of biomechanics, computer animation, human\ud computer interaction and behavior science come together to discus their methods to both record motion and\ud to extract useful properties from the data. In these fields, the construction of human movement models from\ud motion capture data is the focal point, although the application of such models differs per field. Such\ud models can be used to generate and evaluate highly adaptable and believable animation on virtual\ud characters in computer animation, to explore the details of gesture interaction in Human Computer\ud Interaction applications, to identify patterns related to affective states or to find biomechanical properties of\ud human movement

Gait Recognition from Motion Capture Data

Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU MoCap database show that the suggested method outperforms thirteen relevant methods based on geometric features and a method to learn the features by a combination of Principal Component Analysis and Linear Discriminant Analysis. The methods are evaluated in terms of the distribution of biometric templates in respective feature spaces expressed in a number of class separability coefficients and classification metrics. Results also indicate a high portability of learned features, that means, we can learn what aspects of walk people generally differ in and extract those as general gait features. Recognizing people without needing group-specific features is convenient as particular people might not always provide annotated learning data. As a contribution to reproducible research, our evaluation framework and database have been made publicly available. This research makes motion capture technology directly applicable for human recognition.Comment: Preprint. Full paper accepted at the ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans. 18 pages. arXiv admin note: substantial text overlap with arXiv:1701.00995, arXiv:1609.04392, arXiv:1609.0693

Markerless Motion Capture in the Crowd

This work uses crowdsourcing to obtain motion capture data from video recordings. The data is obtained by information workers who click repeatedly to indicate body configurations in the frames of a video, resulting in a model of 2D structure over time. We discuss techniques to optimize the tracking task and strategies for maximizing accuracy and efficiency. We show visualizations of a variety of motions captured with our pipeline then apply reconstruction techniques to derive 3D structure.Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991

Motion capture

Motion capture systems have mostly been developed to record the movements performed by human beings. The main use of these systems is in computer animation, for the creation of animated characters in video games or in movies, but these systems are used too for therapeutic purposes or for professional sportsmen/women

Human Motion Capture Data Tailored Transform Coding

Human motion capture (mocap) is a widely used technique for digitalizing human movements. With growing usage, compressing mocap data has received increasing attention, since compact data size enables efficient storage and transmission. Our analysis shows that mocap data have some unique characteristics that distinguish themselves from images and videos. Therefore, directly borrowing image or video compression techniques, such as discrete cosine transform, does not work well. In this paper, we propose a novel mocap-tailored transform coding algorithm that takes advantage of these features. Our algorithm segments the input mocap sequences into clips, which are represented in 2D matrices. Then it computes a set of data-dependent orthogonal bases to transform the matrices to frequency domain, in which the transform coefficients have significantly less dependency. Finally, the compression is obtained by entropy coding of the quantized coefficients and the bases. Our method has low computational cost and can be easily extended to compress mocap databases. It also requires neither training nor complicated parameter setting. Experimental results demonstrate that the proposed scheme significantly outperforms state-of-the-art algorithms in terms of compression performance and speed