  Tourists and Wild-life photographers are often hindered in capturing their
cherished images or videos by a fence that limits accessibility to the scene of
interest. The situation has been exacerbated by growing concerns of security at
public places and a need exists to provide a tool that can be used for
post-processing such fenced videos to produce a de-fenced image. There are
several challenges in this problem, we identify them as Robust detection of
fence/occlusions and Estimating pixel motion of background scenes and Filling
in the fence/occlusions by utilizing information in multiple frames of the
input video. In this work, we aim to build an automatic post-processing tool
that can efficiently rid the input video of occlusion artifacts like fences.
Our work is distinguished by two major contributions. The first is the
introduction of learning based technique to detect the fences patterns with
complicated backgrounds. The second is the formulation of objective function
and further minimization through loopy belief propagation to fill-in the fence
pixels. We observe that grids of Histogram of oriented gradients descriptor
using Support vector machines based classifier significantly outperforms
detection accuracy of texels in a lattice. We present results of experiments
using several real-world videos to demonstrate the effectiveness of the
proposed fence detection and de-fencing algorithm.
