Multi-modal RGB-Depth-Thermal Human Body Segmentation.
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| Title: | Multi-modal RGB-Depth-Thermal Human Body Segmentation. |
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| Authors: | Palmero, Cristina c.palmero.cantarino@gmail.com, Clapés, Albert aclapes@cvc.uab.cat, Bahnsen, Chris1 cb@create.aau.dk, Møgelmose, Andreas1 am@create.aau.dk, Moeslund, Thomas1 tbmg@create.aau.dk, Escalera, Sergio sergio@maia.ub.es |
| Source: | International Journal of Computer Vision. Jun2016, Vol. 118 Issue 2, p217-239. 23p. |
| Subjects: | Human body, Mathematical models of human behavior, Human behavior models, Supervised learning, Boosting algorithms, Mathematical models |
| 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 segmentation baseline. The several modalities are registered using a calibration device and a registration algorithm. Our baseline extracts regions of interest using background subtraction, defines a partitioning of the foreground regions into cells, computes a set of image features on those cells using different state-of-the-art feature extractions, and models the distribution of the descriptors per cell using probabilistic models. A supervised learning algorithm then fuses the output likelihoods over cells in a stacked feature vector representation. 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 methods, obtaining an overlap above 75 % on the novel dataset when compared to the manually annotated ground-truth of human segmentations. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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