Motion Images: An Effective Representation of Motion Capture Data for Similarity Search
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Proceedings |
| Conference | Proceedings of 8th International Conference on Similarity Search and Applications (SISAP 2015), LNCS 9371 |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1007/978-3-319-25087-8_24 |
| Field | Informatics |
| Keywords | motion capture data; motion similarity; visualization; motion image; action classification |
| Description | The rapid development of motion capturing technologies has caused a massive usage of human motion data in a variety of fields, such as computer animation, gaming industry, medicine, sports and security. These technologies produce large volumes of complex spatio-temporal data which need to be effectively compared on the basis of similarity. In contrast to a traditional way of extracting numerical features, we propose a new idea to transform complex motion data into RGB images and compare them by content-based image retrieval methods. We see transformed RGB images as suitable application-independent features for their ability to preserve key aspects of performed motions. To demonstrate the usability of this idea, we evaluate a preliminary experiment that classifies 1,034 motions into 14 categories with the 87.4% precision. |
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