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Change detection for moving object segmentation with robust background construction under Wronskian framework

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Abstract

Although background subtraction techniques have been used for several years in vision systems for moving object detection, many of them fail to provide good results in presence of noise, illumination variation, non-static background, etc. A basic requirement of background subtraction scheme is the construction of a stable background model and then comparing each incoming image frame with it so as to detect moving objects. The novelty of the proposed scheme is to construct a stable background model from a given video sequence dynamically. The constructed background model is compared with different image frames of the same sequence to detect moving objects. In the proposed scheme the background model is constructed by analyzing a sequence of linearly dependent past image frames in Wronskian framework. The Wronskian based change detection model is further used to detect the changes between the constructed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background, and it is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spatial region of support in this regard. The proposed scheme relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spatial and temporal modes. The results obtained by the proposed model is found to provide accurate shape of moving objects. The effectiveness of the proposed scheme is verified by comparing the results with those of some of the existing state of the art background subtraction techniques on public benchmark databases. We found that the average F-measure is significantly improved by the proposed scheme from that of the state-of-the-art techniques.

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Acknowledgments

The authors like to acknowledge the through and constructive comments provided by the reviewers and the editor on this paper. Badri Narayan Subudhi acknowledges the Council of Scientific and Industrial Research (CSIR) for providing a senior research fellowship (No. 9/93 (0137)/ 2011) to him.

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Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, 700108, India

    Badri Narayan Subudhi & Ashish Ghosh

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

    Susmita Ghosh

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  1. Badri Narayan Subudhi
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  2. Susmita Ghosh
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  3. Ashish Ghosh
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Correspondence to Ashish Ghosh.

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Subudhi, B.N., Ghosh, S. & Ghosh, A. Change detection for moving object segmentation with robust background construction under Wronskian framework. Machine Vision and Applications 24, 795–809 (2013). https://doi.org/10.1007/s00138-012-0475-8

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  • DOI: https://doi.org/10.1007/s00138-012-0475-8

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