Summary of Detecting Car Speed Using Object Detection and Depth Estimation: a Deep Learning Framework, by Subhasis Dasgupta et al.
Detecting Car Speed using Object Detection and Depth Estimation: A Deep Learning Framework
by Subhasis Dasgupta, Arshi Naaz, Jayeeta Choudhury, Nancy Lahiri
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 project aims to develop a handheld device for estimating vehicle speeds using deep learning frameworks on mobile phones or wearable cameras. The goal is to control over speeding, which is a common cause of fatal accidents worldwide. Existing devices like LIDAR-based and Radar-based guns are not always available to traffic police, making this solution essential. The project leverages deep learning algorithms to estimate speed based on camera input, providing an accurate and real-time measure for traffic enforcement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team is working on a new device that can guess how fast cars are going using just a phone or special camera. This is important because many car crashes happen when people drive too fast. Right now, police don’t always have the right tools to check speeds, so this project hopes to fix that problem. The idea is to use super-smart computer programs to look at what the camera sees and figure out how fast the car is moving. |
Keywords
* Artificial intelligence * Deep learning
