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The quality of recorded videos and images is significantly influenced by thecamera's field of view (FOV). In critical applications like surveillancesystems and self-driving cars, an inadequate FOV can give rise to severe safetyand security concerns, including car accidents and thefts due to the failure todetect individuals and objects. The conventional methods for establishing thecorrect FOV heavily rely on human judgment and lack automated mechanisms toassess video and image quality based on FOV. In this paper, we introduce aninnovative approach that harnesses semantic line detection and classificationalongside deep Hough transform to identify semantic lines, thus ensuring asuitable FOV by understanding 3D view through parallel lines. Our approachyields an effective F1 score of 0.729 on the public EgoCart dataset, coupledwith a notably high median score in the line placement metric. We illustratethat our method offers a straightforward means of assessing the quality of thecamera's field of view, achieving a classification accuracy of 83.8\%. Thismetric can serve as a proxy for evaluating the potential performance of videoand image quality applications.

Enhancing Surveillance Camera FOV Quality via Semantic Line Detection and Classification with Deep Hough Transform