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 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 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.
['Laura Cabrera-Quiros', 'Hayley Hung']
Who is where?: Matching People in Video to Wearable Acceleration During Crowded Mingling Events