Summary of Sevd: Synthetic Event-based Vision Dataset For Ego and Fixed Traffic Perception, by Manideep Reddy Aliminati et al.
SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception
by Manideep Reddy Aliminati, Bharatesh Chakravarthi, Aayush Atul Verma, Arpitsinh Vaghela, Hua Wei, Xuesong Zhou, Yezhou Yang
First submitted to arxiv on: 12 Apr 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 The paper presents SEVD, a novel multi-view ego and fixed perception synthetic event-based dataset designed to address the limitations of conventional RGB cameras in handling challenging dynamic conditions. The dataset is created using multiple dynamic vision sensors within the CARLA simulator, featuring diverse lighting, weather, and domain shifts scenarios. It includes event data alongside RGB imagery, depth maps, optical flow, semantic, and instance segmentation, facilitating a comprehensive understanding of the scene. State-of-the-art event-based (RED, RVT) and frame-based (YOLOv8) methods are evaluated for traffic participant detection tasks, providing baseline benchmarks for assessment. The paper also assesses the synthetic dataset’s generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new dataset called SEVD to help autonomous vehicles see better in different conditions like day or night, rain or shine. They used special cameras and a computer simulator to make the dataset. It includes lots of information about what the camera sees, like pictures, depth maps, and motion tracking. The researchers tested some popular AI models on this dataset and found that they can detect traffic participants pretty well. This is important because it helps self-driving cars understand their surroundings. |
Keywords
» Artificial intelligence » Generalization » Instance segmentation » Optical flow » Tracking
