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 these images not only as human-understandable visualization of motion characteristics e.g., speed, duration and movement repetitions, but also as descriptive 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,i¾?034 motions into 14 categories with the $$87.4\,\%$$ precision.
['Petr Elias', 'Jan Sedmidubsky', 'Pavel Zezula']
Motion Images: An Effective Representation of Motion Capture Data for Similarity Search