Abstract:
Moving object segmentation (MOS) in different practical scenarios like weather degraded, dynamic background,
etc. videos is a challenging and high demanding task for
various computer vision applications. Existing supervised
approaches achieve remarkable performance with complicated training or extensive fine-tuning or inappropriate
training-testing data distribution. Also, the generalized effect of existing works with completely unseen data is difficult to identify. In this work, the recurrent feature sharing
based generative adversarial network is proposed with unseen video analysis. The proposed network comprises of
dilated convolution to extract the spatial features at multiple scales. Along with the temporally sampled multiple
frames, previous frame output is considered as input to the
network. As the motion is very minute between the two consecutive frames, the previous frame decoder features are
shared with encoder features recurrently for current frame
foreground segmentation. This recurrent feature sharing of
different layers helps the encoder network to learn the hierarchical interactions between the motion and appearancebased features. Also, the learning of the proposed network
is concentrated in different ways, like disjoint and global
training-testing for MOS. An extensive experimental analysis of the proposed network is carried out on two benchmark
video datasets with seen and unseen MOS video. Qualitative and quantitative experimental study shows that the proposed network outperforms the existing methods.
