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

arXiv: https://arxiv.org/abs/2401.09515 · HTML: https://mlsanity.com/paper/2401.09515/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [End-to-End Wireframe Parsing](https://mlsanity.com/paper/1905.03246/) (2019)
- [Deep Hough Transform for Semantic Line Detection](https://mlsanity.com/paper/2003.04676/) (2020)
- [ELSD: Efficient Line Segment Detector and Descriptor](https://mlsanity.com/paper/2104.14205/) (2021)
- [Hole-robust Wireframe Detection](https://mlsanity.com/paper/2111.15064/) (2021)
- [Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching](https://mlsanity.com/paper/2203.15285/) (2022)
- [Semantic Room Wireframe Detection from a Single View](https://mlsanity.com/paper/2206.00491/) (2022)
- [Harmonious Semantic Line Detection via Maximal Weight Clique Selection](https://mlsanity.com/paper/2104.06903/) (2021)

## Abstract (arXiv)

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.