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Multi-modal RGB–Depth–Thermal Human Body Segmentation

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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.

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Notes

  1. This is an implementation of the work of Bradski and Kaehler (2008), which can be found at http://code.opencv.org.

  2. http://pointclouds.org/documentation/tutorials/gpu_people.php.

  3. https://github.com/PointCloudLibrary/data/tree/master/people/results.

  4. Shotton et al. (2011) specified in the “Acknowledgements” section that the tracking system of Kinect SDK was built based on the research they presented in the paper.

  5. Check the video included as supplementary material in which some qualitative results are shown, named trimodal_seg_results.mp4.

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Acknowledgments

This work was partly supported by the Spanish Project TIN2013-43478-P. The work of Albert Clapés was supported by SUR-DEC of the Generalitat de Catalunya and FSE. We would like to thank Anders Jørgensen for his valuable help in capturing the dataset.

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Authors and Affiliations

  1. Dept. Matemàtica Aplicada i Anàlisi, UB, Gran Via de les Corts Catalanes 585, 08007, Barcelona, Spain

    Cristina Palmero, Albert Clapés & Sergio Escalera

  2. Computer Vision Center, Campus UAB, Edifici O, 08193, Cerdanyola del Vallès, Spain

    Cristina Palmero, Albert Clapés & Sergio Escalera

  3. Aalborg University, Sofiendalsvej 11, 9200, Aalborg SV, Denmark

    Chris Bahnsen, Andreas Møgelmose & Thomas B. Moeslund

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  1. Cristina Palmero
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  2. Albert Clapés
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  4. Andreas Møgelmose
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Correspondence to Cristina Palmero.

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Communicated by Junsong Yuan, Wanqing Li, Zhengyou Zhang, David Fleet, Jamie Shotton.

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Palmero, C., Clapés, A., Bahnsen, C. et al. Multi-modal RGB–Depth–Thermal Human Body Segmentation. Int J Comput Vis 118, 217–239 (2016). https://doi.org/10.1007/s11263-016-0901-x

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