Abstract:
The underwater moving object segmentation is a challenging
task. The problems like absorbing, scattering and attenuation
of light rays between the scene and the imaging platform
degrades the visibility of image or video frames. Also, the
back-scattering of light rays further increases the problem of
underwater video analysis, because the light rays interact with
underwater particles and scattered back to the sensor. In this
paper, a novel Motion Saliency Based Generative Adversarial
Network (GAN) for Underwater Moving Object Segmentation (MOS) is proposed. The proposed network comprises of
both identity mapping and dense connections for underwater
MOS. To the best of our knowledge, this is the first paper
with the concept of GAN-based unpaired learning for MOS
in underwater videos. Initially, current frame motion saliency
is estimated using few initial video frames and current frame.
Further, estimated motion saliency is given as input to the
proposed network for foreground estimation. To examine the
effectiveness of proposed network, the Fish4Knowledge [1]
underwater video dataset and challenging video categories of
ChangeDetection.net-2014 [2] datasets are considered. The
segmentation accuracy of existing state-of-the-art methods
are used for comparison with proposed approach in terms of
average F-measure. From experimental results, it is evident
that the proposed network shows significant improvement as
compared to the existing state-of-the-art methods for MOS.
