Summary of Woodscape Motion Segmentation For Autonomous Driving — Cvpr 2023 Omnicv Workshop Challenge, by Saravanabalagi Ramachandran and Nathaniel Cibik and Ganesh Sistu and John Mcdonald
WoodScape Motion Segmentation for Autonomous Driving – CVPR 2023 OmniCV Workshop Challenge
by Saravanabalagi Ramachandran, Nathaniel Cibik, Ganesh Sistu, John McDonald
First submitted to arxiv on: 31 Dec 2023
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 A novel motion segmentation challenge is introduced in this paper, specifically designed for autonomous driving applications that utilize fisheye cameras. The challenge arises from the complex interplay between camera ego-motion, radial distortion, and temporal consistency requirements, which traditional CNN approaches struggle to address effectively. To overcome these challenges, the authors employ a synthetic dataset, PD-WoodScape, in conjunction with the WoodScape fisheye dataset. This study aims to evaluate the potential of using synthetic data in this domain and explores the complexities inherent in motion segmentation tasks. The analysis is based on a competition that attracted 112 global teams and 234 submissions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous driving is getting more complex, especially when it comes to cameras that move around. This makes it hard for computers to separate moving objects from the background. To make things even harder, there’s also distortion in the camera lenses and we need to keep track of what’s happening over time. The traditional way of doing this doesn’t work very well, so scientists created fake data to help improve their models. They used a special dataset called PD-WoodScape along with some real data from WoodScape. This study shows how important it is to use this fake data and explores the challenges in motion segmentation. |
Keywords
* Artificial intelligence * Cnn * Synthetic data
