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A Benchmarking Framework for Background Subtraction in RGBD Videos

  • Massimo Camplani
  • Lucia Maddalena
  • Gabriel Moyá Alcover
  • Alfredo Petrosino
  • Luis Salgado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10590)

Abstract

The complementary nature of color and depth synchronized information acquired by low cost RGBD sensors poses new challenges and design opportunities in several applications and research areas. Here, we focus on background subtraction for moving object detection, which is the building block for many computer vision applications, being the first relevant step for subsequent recognition, classification, and activity analysis tasks. The aim of this paper is to describe a novel benchmarking framework that we set up and made publicly available in order to evaluate and compare scene background modeling methods for moving object detection on RGBD videos. The proposed framework involves the largest RGBD video dataset ever made for this specific purpose. The 33 videos span seven categories, selected to include diverse scene background modeling challenges for moving object detection. Seven evaluation metrics, chosen among the most widely used, are adopted to evaluate the results against a wide set of pixel-wise ground truths. Moreover, we present a preliminary analysis of results, devoted to assess to what extent the various background modeling challenges pose troubles to background subtraction methods exploiting color and depth information.

Keywords

Background subtraction Color and depth data RGBD 

Notes

Acknowledgments

We would like to thank all the authors who submitted their results to the SBM-RGBD Challenge, which will serve as reference for future generation methods. L. Maddalena wishes to acknowledge the GNCS (Gruppo Nazionale di Calcolo Scientifico) and the INTEROMICS Flagship Project funded by MIUR, Italy. A. Petrosino wishes to acknowledge Project VIRTUALOG Horizon 2020-PON 2014/2020. L. Salgado wishes to acknowledge projects TEC2013-48453 (MR-UHDTV) and TEC2016-75981 (IVME) funded by the Ministerio de Economa, Industria y Competitividad (AEI/FEDER) of the Spanish Government.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Massimo Camplani
    • 1
  • Lucia Maddalena
    • 2
  • Gabriel Moyá Alcover
    • 3
  • Alfredo Petrosino
    • 4
  • Luis Salgado
    • 5
    • 6
  1. 1.University of BristolBristolUK
  2. 2.National Research CouncilNaplesItaly
  3. 3.Universitat de les Illes BalearsPalmaSpain
  4. 4.University of Naples ParthenopeNaplesItaly
  5. 5.Universidad Politécnica de MadridMadridSpain
  6. 6.Universidad Autónoma de MadridMadridSpain

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