DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Kisoo | ko |
dc.contributor.author | Kim, Hyunju | ko |
dc.contributor.author | Lee, Dongman | ko |
dc.date.accessioned | 2022-10-19T12:00:43Z | - |
dc.date.available | 2022-10-19T12:00:43Z | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.issued | 2022-06 | - |
dc.identifier.citation | 46th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2022, pp.972 - 981 | - |
dc.identifier.uri | http://hdl.handle.net/10203/299050 | - |
dc.description.abstract | Activity Segmentation, dividing a continuous sensor stream into a set of activity segments, is a crucial pre-process in Human Activity Recognition (HAR) and it is required to be done in real-time for real-world smart services. Existing single-user activity segmentation schemes fail to correctly detect transition points due to concurrent and overlapping events from multiple users in case of Multi-user Collaborative Activity Recognition (MCAR). In this paper, we propose a novel scheme for activity segmentation for MCAR that expresses complex events and the correlations between them. For this, the proposed scheme first creates an event stream from a sensor stream and defines event sets in terms of time windows. For each time window, two types of correlations for every event pair are calculated: duration correlation and history correlation. After calculating event correlation, the change score of a time window is measured by comparing the calculated correlation values with those of the preceding windows. Then, the proposed scheme elects as an activity transition point a time window whose change score exceeds the transition threshold. We evaluate the proposed method on two multi-user collaborative activity datasets and experiment results show that the proposed scheme achieves better segmentation performance than existing approaches. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | A Correlation-based Real-time Segmentation Scheme for Multi-user Collaborative Activities | - |
dc.type | Conference | - |
dc.identifier.wosid | 000855983300142 | - |
dc.identifier.scopusid | 2-s2.0-85136922459 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 972 | - |
dc.citation.endingpage | 981 | - |
dc.citation.publicationname | 46th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2022 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1109/COMPSAC54236.2022.00150 | - |
dc.contributor.localauthor | Lee, Dongman | - |
dc.contributor.nonIdAuthor | Kim, Kisoo | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.