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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Baumann, Jan Wessel, Raoul Kruger, Bjorn Weber, Andreas |
| Copyright Year | 2014 |
| Description | Author affiliation: Department of Computer Science II, University of Bonn, Bonn, Germany (Baumann, Jan; Wessel, Raoul; Kruger, Bjorn; Weber, Andreas) |
| Abstract | This work presents a novel and generic data-driven method for recognizing human full body actions from live motion data originating from various sources. The method queries an annotated motion capture database for similar motion segments, capable to handle temporal deviations from the original motion. The approach is online-capable, works in realtime, requires virtually no preprocessing and is shown to work with a variety of feature sets extracted from input data including positional data, sparse accelerometer signals, skeletons extracted from depth sensors and even video data. Evaluation is done by comparing against a frame-based Support Vector Machine approach on a freely available motion capture database as well as a database containing Judo referee signal motions and concludes by demonstrating the applicability of the method in a vision-based scenario using video data. |
| Starting Page | 1 |
| Ending Page | 10 |
| File Size | 2493459 |
| Page Count | 10 |
| File Format | |
| ISBN | 9789897580789 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-01-05 |
| Publisher Place | Portugal |
| Access Restriction | Subscribed |
| Rights Holder | SCITEPRESS |
| Subject Keyword | Support vector machines Accelerometers Training Databases Motion Capture Motion segmentation Accelerometer Animation Skeleton Action Recognition Sensors |
| Content Type | Text |
| Resource Type | Article |
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