Enhancing Surveillance Camera FOV Quality via Semantic Line Detection
  and Classification with Deep Hough Transform
Andrew C. Freeman Wenjing Shi Bin Hwang
The quality of recorded videos and images is significantly influenced by the
camera's field of view (FOV). In critical applications like surveillance
systems and self-driving cars, an inadequate FOV can give rise to severe safety
and security concerns, including car accidents and thefts due to the failure to
detect individuals and objects. The conventional methods for establishing the
correct FOV heavily rely on human judgment and lack automated mechanisms to
assess video and image quality based on FOV. In this paper, we introduce an
innovative approach that harnesses semantic line detection and classification
alongside deep Hough transform to identify semantic lines, thus ensuring a
suitable FOV by understanding 3D view through parallel lines. Our approach
yields an effective F1 score of 0.729 on the public EgoCart dataset, coupled
with a notably high median score in the line placement metric. We illustrate
that our method offers a straightforward means of assessing the quality of the
camera's field of view, achieving a classification accuracy of 83.8\%. This
metric can serve as a proxy for evaluating the potential performance of video
and image quality applications.