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Online sequential extreme learning machine-based co-training for dynamic moving cast shadow detection

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Abstract

Cast shadow detection and removal is one of the key problems in vision-based systems for accurate and robust segmentation of moving objects. This paper proposes a co-training-based adaptive method for detecting moving shadows in video sequences. Shadow detection based on static methods cannot adapt to changing environment such as gradual illumination changes. In order to solve this problem, we have proposed an online sequential extreme learning machine (OS-ELM)-based semi-supervised technique for moving cast shadow detection. Online learning of OS-ELM is much faster and provides better generalization performance compared to other popular online learning algorithms. First, we extracted color, texture, gradient, and image patch similarity features using a background model and input video frame, which are useful for discriminating moving shadows and objects. Co-training scheme is used for online updating of the OS-ELM classifier in order to adapt to the dynamic environment. Experimental results on different benchmark video sequences shows that the proposed method performs better shadow detection and discrimination compared with other methods.

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Authors and Affiliations

  1. Korea Electronics Technology Institute, Jeonju-si, Jeollabuk-do, 561-844, Korea

    Deepak Ghimire

  2. Division of Computer Engineering, Chonbuk National University, Jeonju-si, Jeollabuk-do, 561-756, Korea

    Joonwhoan Lee

Authors
  1. Deepak Ghimire
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  2. Joonwhoan Lee
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Correspondence to Joonwhoan Lee.

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Ghimire, D., Lee, J. Online sequential extreme learning machine-based co-training for dynamic moving cast shadow detection. Multimed Tools Appl 75, 11181–11197 (2016). https://doi.org/10.1007/s11042-015-2839-3

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  • DOI: https://doi.org/10.1007/s11042-015-2839-3

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