Who is where? Matching people in video to wearable acceleration during crowded mingling events
Resumen
We address the challenging problem of associating acceler-
ation data from a wearable sensor with the corresponding
spatio-temporal region of a person in video during crowded
mingling scenarios. This is an important rst step for multi-
sensor 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 hierarchi-
cal association approach which exploits the spatial context
of the scene, outperforming the state-of-the-art approaches
signi cantly. Moreover, we present experiments on match-
ing from 3 to more than 130 acceleration and video streams
which, to our knowledge, is signi cantly larger than prior
works where only up to 5 device streams are associated.
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MM 2016 - Proceedings of the 2016 ACM Multimedia ConferenceCompartir
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- Artículos [15]