Publication details

A Real-Time Annotation of Motion Data Streams



Year of publication 2017
Type Article in Proceedings
Conference 19th IEEE International Symposium on Multimedia
MU Faculty or unit

Faculty of Informatics

Field Informatics
Keywords motion capture data; motion data stream; real-time annotation; motion profiles; online segmentation; similarity measure; deep neural network
Description Current motion-capture technologies produce continuous streams of 3D human joint trajectories. One of the challenges is to automatically annotate such streams of complex spatio-temporal data in real time. In this paper, we propose an efficient approach to label motion stream data in real time with a limited usage of main memory. Based on a set of user-defined motion profiles, each of them specified by multiple representative samples, the currently visible part of an input motion stream is processed by identifying a moderate number of segments of various lengths. These segments are compared to the profiles to measure their similarity. The segments having a high similarity to a given motion profile are annotated with the corresponding label. The proposed approach performs fast, allows profiles to be dynamically changed at runtime, and does not require any learning procedure, in comparison with existing solutions evaluated on real-life data.
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