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Hydraulic Excavators Recognition Based on Inverse ”V” Feature of Mechanical Arm

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Pattern Recognition (CCPR 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 484))

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Abstract

Detecting hydraulic excavators in videos can increase the confidence coefficient of illegal construction in nationalized land. Hydraulic Excavators have multifarious working postures making them a difficult target using even state of the art object recognition algorithms. The contribution of this paper is to propose an inverse ”V” model for hydraulic excavator detection. In this paper, we describe an hydraulic excavator detection system based on inverse ”V” feature of mechanical arm which is formed by boom and dipper and show a detection system. Then a real-time video processing method is presented which is used for monitoring illegal construction activities on a land of state-ownership.

This work was supported by the Science Industry Trade and Information Technology Commission of Shenzhen Municipality under Grant JCYJ20130402145002441.

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Author information

Authors and Affiliations

  1. Shenzhen Key Lab. of Information Sci & Tech / Shenzhen Engineering Lab. of IS & DRM, Department of Electronic Engineering / Graduate School at Shenzhen, Tsinghua University, China

    Wenming Yang, Dedi Li, Daren Sun & Qingmin Liao

Authors
  1. Wenming Yang
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  2. Dedi Li
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  3. Daren Sun
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  4. Qingmin Liao
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Editor information

Editors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, 410082, Changsha, P.R. China

    Shutao Li

  2. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  3. College of electrical and information engineering, Hunan University, 410082, Changsha, P.R. China

    Yaonan Wang

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© 2014 Springer-Verlag Berlin Heidelberg

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Yang, W., Li, D., Sun, D., Liao, Q. (2014). Hydraulic Excavators Recognition Based on Inverse ”V” Feature of Mechanical Arm. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_57

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  • DOI: https://doi.org/10.1007/978-3-662-45643-9_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45642-2

  • Online ISBN: 978-3-662-45643-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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