Summary of Greeneye: Development Of Real-time Traffic Signal Recognition System For Visual Impairments, by Danu Kim
GreenEye: Development of Real-Time Traffic Signal Recognition System for Visual Impairments
by Danu Kim
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed GreenEye system uses machine learning techniques to recognize traffic signals’ color and determine the time left for pedestrians to cross the crosswalk in real-time. The method is designed specifically to help visually impaired people navigate through intersections safely. While previous research has focused on recognizing only two traffic signals, green and red lights, GreenEye can identify four classes of traffic signals with a precision rate of 99.5%. The system’s training data was imbalanced, which affected its initial performance, but after stabilization, it achieved an impressive accuracy rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The GreenEye system is designed to help visually impaired people recognize traffic signals and determine the time left to cross the street. It uses machine learning techniques to identify four classes of traffic signals with a high level of accuracy. The system’s training data was initially imbalanced, but after adjustments were made, it performed well. |
Keywords
» Artificial intelligence » Machine learning » Precision
