A Vision Based Fractional Order TV-Model for Underwater Motion Estimation
Muzammil Khan, Pushpendra Kumar · 2021 IEEE Bombay Section Signature Conference (IBSSC) · 2021
As we are aware that underwater motion estimation is an active and challenging area of vision system which belongs to the category of robot navigation. In particular, motion detection and tracking in underwater image sequences is carried out based on optical flow. In this paper, a vision based underwater navigation system is developed based on the fractional order total variation (TV) model. The objective of this work is to design a variational model by using a quadratic data and total variation terms to provide the optimal performance against radiometric characteristics such as turbidity, non-uniform illumination and marine snow, etc. The presented ameliorated model is more robust against outliers and reduces the problem of local minima. The fractional derivative discretization of non-differentiable terms is performed using Grünwald-Letnikov derivative scheme. Finally, the resulting variational formulation is solved by using an appropriate method. The validity, efficiency, and robustness of the proposed model are tested on a variety of datasets.