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Transactions on Edutainment XIV pp 40–55Cite as

3D Indoor Environment Modeling and Detection of Moving Object and Scene Understanding

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Part of the book series: Lecture Notes in Computer Science ((TEDUTAIN,volume 10790))

Abstract

Obtaining large-scale outdoor city environment is a mature technique, supporting many applications, such as search, navigation, etc. The indoor environment with same complexity often contains high density duplicate objects (e.g. table, chair, display, etc.). In this paper, special structure of indoor environment was applied to accelerate home video camera to conduct 3D collection and identification of indoor environment. There are two stages of this method: (i) Learning stage, gain three-dimensional model of objects which occurs frequently and variation pattern with only few scanning capture, (ii) identification stage, determine the objects which had been seen before but in different gestures and positions from the single scanning of a new field, which greatly accelerate identification process.

In addition, under indoor environment, detection of moving objects, such as lighting change, is also arduous task. Basic histons and histons roughness index (HRI) related to it have been reported by literature. It has impressive achievement for the segmentation of still image. We expand histons definition to 3D histons and consider the combined mode of the whole color plane, but not to consider single color plane. Besides, bring fuzziness into 3D HRI measurement. Move the object in accordance with the concept of rough set theory, change background gradually or set dynamic background as background, and pixel labeled at the same time won’t let the objects that move slowly bring into background model.

Common home video camera was used to obtain typical area of a university construction, including classroom and laboratory, to prove result of the method we adopted, by qualitative and quantitative analysis, was satisfying.

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Acknowledgement

This research is based upon work supported in part by National Natural Science Foundation of China (61370173), in part by Science and Technology Project of Zhejiang Province (2014C31084), and in part by Science and Technology Project of Huzhou City (2013GZ02).

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Authors and Affiliations

  1. School of Information Engineering, Huzhou University, Huzhou, 313000, China

    Bin Shao

  2. State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, 310027, China

    Bin Shao

  3. School of Teachers’ Education, Huzhou University, Huzhou, 313000, Zhejiang, China

    Zhimin Yan

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  1. Bin Shao
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  2. Zhimin Yan
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Correspondence to Bin Shao .

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Editors and Affiliations

  1. Hangzhou Normal University, Hangzhou, China

    Zhigeng Pan

  2. Graduate School of Media Design, Keio University, Yokohama-shi, Japan

    Adrian David Cheok

  3. University of Education, Weingarten, Germany

    Wolfgang Müller

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Shao, B., Yan, Z. (2018). 3D Indoor Environment Modeling and Detection of Moving Object and Scene Understanding. In: Pan, Z., Cheok, A., Müller, W. (eds) Transactions on Edutainment XIV. Lecture Notes in Computer Science(), vol 10790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56689-3_4

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  • DOI: https://doi.org/10.1007/978-3-662-56689-3_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-56688-6

  • Online ISBN: 978-3-662-56689-3

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