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A Novel Saliency-Based Cascaded Approach for Moving Object Segmentation

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Computer Vision and Image Processing (CVIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1147))

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Abstract

The existing approaches achieved remarkable performance in many computer vision applications like moving object segmentation (MOS), classification, etc. However, in presence of infrequent motion of foreground objects, bad weather and dynamic background, the accurate foreground-background segmentation is a tedious task. In addition, the computational complexity is a major concern, as the data to be processed is large in case of video analysis. Considering the above mentioned problems, a novel compact motion saliency based cascaded encoder-decoder network is proposed for MOS. To estimate the motion saliency of current frame, background image is estimated using few neighbourhood frames and subtracted from the current frame. Further, to estimate prior foreground probability maps compact encoder-decoder network is proposed. The estimated foreground probability maps are undergoes the problem of spatial coherence where visibility of foreground objects is not clear. To enhance the spatial coherence of obtained foreground probability map, cascaded encoder-decoder network is incorporated. The intensive experimentation is carried out to investigate the efficiency of proposed network with different challenging videos from CDnet-2014 and PTIS database. The segmentation accuracy is verified and compared with existing method in terms of average F-measure. In addition, the compactness of proposed method is analysed in terms of computational complexity and compared with the existing methods. The performance of proposed method is significantly improved as compared to existing methods in terms of accuracy and computational complexity for MOS task.

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Authors and Affiliations

  1. CVPR Lab, Indian Institute of Technology Ropar, Rupnagar, India

    Prashant W. Patil, Akshay Dudhane & Subrahmanyam Murala

  2. SGGSIET, Nanded, MS, India

    Anil B. Gonde

Authors
  1. Prashant W. Patil
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  2. Akshay Dudhane
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  3. Subrahmanyam Murala
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  4. Anil B. Gonde
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Corresponding author

Correspondence to Prashant W. Patil .

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Editors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Neeta Nain

  2. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Santosh Kumar Vipparthi

  3. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

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Patil, P.W., Dudhane, A., Murala, S., Gonde, A.B. (2020). A Novel Saliency-Based Cascaded Approach for Moving Object Segmentation. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1147. Springer, Singapore. https://doi.org/10.1007/978-981-15-4015-8_28

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  • DOI: https://doi.org/10.1007/978-981-15-4015-8_28

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  • Print ISBN: 978-981-15-4014-1

  • Online ISBN: 978-981-15-4015-8

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