Abstract
This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB-Depth-Thermal dataset along with a multi-modal seg- mentation baseline. The several modalities are registered us- ing a calibration device and a registration algorithm. Our baseline extracts regions of interest using background sub- traction, defines a partitioning of the foreground regions into cells, computes a set of image features on those cells us- ing different state-of-the-art feature extractions, and models the distribution of the descriptors per cell using probabilis- tic models. A supervised learning algorithm then fuses the output likelihoods over cells in a stacked feature vector rep- resentation. The baseline, using Gaussian Mixture Models for the probabilistic modeling and Random Forest for the stacked learning, is superior to other state-of-the-art meth- ods, obtaining an overlap above 75% on the novel dataset when compared to the manually annotated ground-truth of human segmentations.
Originalsprog | Engelsk |
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Tidsskrift | International Journal of Computer Vision |
Vol/bind | 118 |
Udgave nummer | 2 |
Sider (fra-til) | 217-239 |
ISSN | 0920-5691 |
DOI | |
Status | Udgivet - 13 apr. 2016 |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Multi-modal RGB–Depth–Thermal Human Body Segmentation'. Sammen danner de et unikt fingeraftryk.Forskningsdatasæt
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AAU VAP Trimodal People Segmentation Dataset
Bahnsen, C. H. (Ophavsperson), Møgelmose, A. (Ophavsperson) & Moeslund, T. B. (Ophavsperson), Kaggle, 1 jan. 2017
DOI: 10.34740/kaggle/dsv/396257
Datasæt: Supplerende materiale