Abstract
We address the challenging problem of associating acceleration data from a wearable sensor with the corresponding spatio-temporal region of a person in video during crowded mingling scenarios. This is an important first step for multisensor behavior analysis using these two modalities. Clearly, as the numbers of people in a scene increases, there is also a need to robustly and automatically associate a region of the video with each person’s device. We propose a hierarchical association approach which exploits the spatial context of the scene, outperforming the state-of-the-art approaches significantly. Moreover, we present experiments on matching from 3 to more than 130 acceleration and video streams which, to our knowledge, is significantly larger than prior works where only up to 5 device streams are associated.
Original language | English |
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Title of host publication | Proceedings of the 2016 ACM Multimedia Conference, MM 2016 |
Place of Publication | New York, NY |
Publisher | Association for Computing Machinery (ACM) |
Pages | 267-271 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-4503-3603-1 |
DOIs | |
Publication status | Published - 2016 |
Event | MM'16 the ACM Multimedia Conference: 24th ACM Multimedia Conference - Amsterdam, Netherlands Duration: 15 Oct 2016 → 19 Oct 2016 |
Conference
Conference | MM'16 the ACM Multimedia Conference |
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Country/Territory | Netherlands |
City | Amsterdam |
Period | 15/10/16 → 19/10/16 |
Keywords
- Mingling
- wearable sensor
- computer vision
- association