Summary of Enhancing Surveillance Camera Fov Quality Via Semantic Line Detection and Classification with Deep Hough Transform, by Andrew C. Freeman et al.
Enhancing Surveillance Camera FOV Quality via Semantic Line Detection and Classification with Deep Hough Transform
by Andrew C. Freeman, Wenjing Shi, Bin Hwang
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes an innovative approach to ensure a suitable field of view (FOV) in recorded videos and images, which is critical in surveillance systems and self-driving cars. The conventional methods rely heavily on human judgment and lack automated mechanisms to assess FOV-based video and image quality. The proposed method combines semantic line detection and classification with deep Hough transform to identify parallel lines, allowing for a 3D view understanding and thereby ensuring an adequate FOV. This approach demonstrates high accuracy in classifying the quality of FOV, achieving an F1 score of 0.729 on the EgoCart dataset and a median score in line placement metric. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists have developed a new way to make sure that videos and images are good enough for important applications like car cameras and security systems. Right now, people have to decide what’s good and what’s not based on their own judgment, but this new method uses special computer algorithms to figure it out automatically. It works by finding lines in the video or image and using those lines to understand how the camera is looking at things. This approach has been tested and shown to be very effective, with a high accuracy rate. |
Keywords
* Artificial intelligence * Classification * F1 score
