通过结合深度霍夫变换的语义线检测与分类提升监控摄像机视场质量
Enhancing Surveillance Camera FOV Quality via Semantic Line Detection and Classification with Deep Hough Transform
AI总结：
针对摄像机视场质量缺乏自动评估的问题，提出结合深度Hough变换的语义线检测与分类方法，在EgoCart上取得F1 0.729和83.8%分类准确率。
摘要：
录制视频和图像的质量在很大程度上受到摄像机视场（FOV）的影响。在监控系统和自动驾驶汽车等关键应用中，不合适的FOV可能引发严重的安全与安保问题，包括因未能检测到人员和物体而导致的车祸与盗窃。用于确定正确FOV的传统方法严重依赖人工判断，缺乏基于FOV评估视频和图像质量的自动化机制。本文提出一种创新方法，该方法利用语义线检测和分类以及深度Hough变换来识别语义线，从而通过平行线理解三维视图，确保获得合适的FOV。我们的方法在公开的EgoCart数据集上取得了0.729的有效F1分数，并且在线条放置指标上获得了相当高的中位数分数。我们表明，所提方法提供了一种评估摄像机视场质量的直接手段，实现了83.8%的分类准确率。该指标可作为代理，用于评估视频和图像质量应用的潜在性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
