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A Segmentation Based Robust Fractional Variational Model for Motion Estimation

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Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1776))

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Abstract

In this paper, we introduce a nonlinear robust fractional order variational framework for motion estimation from image sequences (video). Particularly, the presented model provides a generalization of integer order derivative based variational functionals and offers an enhanced robustness against outliers while preserving the discontinuity in the dense flow field. The motion is estimated in the form of optical flow. For this purpose, a level set segmentation based fractional order variational functional composed of a non-quadratic Charbonnier norm and a regularization term is propounded. The non-quadratic Charbonnier norm introduces a noise robust character in the model. The fractional order derivative demonstrates non-locality that makes it competent to deal with discontinuous information about edges and texture. The level set segmentation is carried on the flow field instead of images, which is a union of disjoint and independently moving regions such that each motion region contains objects of equal flow velocity. The resulting fractional order partial differential equations are numerically discretized using Grünwald–Letnikov fractional derivative. The nonlinear formulation is transformed into a linear system which is solved with the help of an efficient numerical technique. The results are evaluated by conducting experiments over a variety of datasets. The accuracy and efficiency of the propounded model is also depicted against recently published works.

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Acknowledgements

The authors Pushpendra Kumar and Nitish Kumar Mahala acknowledge the support of NBHM, Mumbai for grant no. 02011/24/2021 NBHM (R.P)/ R &D- II/8669 and the author Muzammil Khan expresses gratitude to MHRD, New Delhi, Government of India.

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Authors and Affiliations

  1. Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology, Bhopal, 462003, India

    Pushpendra Kumar, Muzammil Khan & Nitish Kumar Mahala

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  1. Pushpendra Kumar
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  2. Muzammil Khan
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  3. Nitish Kumar Mahala
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Correspondence to Muzammil Khan .

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Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

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Kumar, P., Khan, M., Mahala, N.K. (2023). A Segmentation Based Robust Fractional Variational Model for Motion Estimation. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_9

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  • DOI: https://doi.org/10.1007/978-3-031-31407-0_9

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