Summary of Neuflow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices, by Zhiyong Zhang et al.
NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices
by Zhiyong Zhang, Huaizu Jiang, Hanumant Singh
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 NeuFlow architecture efficiently estimates high-accuracy optical flow, a crucial component in robotics, computer vision, and other applications. While recent learning-based methods excel in accuracy, they often come with heavy computation costs. NeuFlow addresses both concerns by employing a global-to-local scheme. Global matching is used to estimate an initial optical flow at 1/16 resolution, capturing large displacements, which is then refined at 1/8 resolution using lightweight CNN layers for better accuracy. The approach demonstrates efficiency improvements on various computing platforms, achieving a notable 10x-80x speedup compared to state-of-the-art methods while maintaining comparable accuracy. NeuFlow achieves around 30 FPS on edge computing platforms, representing a significant breakthrough in deploying complex computer vision tasks like SLAM on small robots. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NeuFlow is an innovative approach that helps computers estimate how objects move between two images. This is important for many applications, including robotics and computer vision. The problem with previous methods is that they can be very slow and require a lot of computing power. NeuFlow solves this by using a new way to match features in the images and then refining those matches using special neural networks. This makes NeuFlow much faster than previous methods while still maintaining good accuracy. In fact, NeuFlow can process 30 frames per second on smaller devices like drones, which is very useful for tasks like mapping and tracking. |
Keywords
» Artificial intelligence » Cnn » Optical flow » Tracking
