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Dynamic background modeling using intensity and orientation distribution of video sequence

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Abstract

Moving object detection in a video sequence is a challenging task in presence of dynamic background. In this paper, we propose a novel approach for background modeling by exploiting orientated patterns present in a video scene. Based on the observation that there exists a difference in directional edge patterns between foreground and background, we use the statistical measures of the orientation of texture via two angle co-occurrence matrices (ACMs). Orientation based features extracted from ACMs are then clubbed with intensity distribution-based features extracted from well-known gray level co-occurrence matrix (GLCM) to model the dynamic background. The model is then used to classify pixels within a video frame into background and foreground. Experimental results on a diverse set of video sequences have shown the effectiveness of the proposed method over competing schemes.

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India

    Rhittwikraj Moudgollya & Arun Kumar Sunaniya

  2. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, 10022, USA

    Abhishek Midya & Jayasree Chakraborty

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  1. Rhittwikraj Moudgollya
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  4. Jayasree Chakraborty
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Correspondence to Rhittwikraj Moudgollya.

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Moudgollya, R., Midya, A., Sunaniya, A.K. et al. Dynamic background modeling using intensity and orientation distribution of video sequence. Multimed Tools Appl 78, 22537–22554 (2019). https://doi.org/10.1007/s11042-019-7575-7

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