Summary of Neuflow V2: High-efficiency Optical Flow Estimation on Edge Devices, by Zhiyong Zhang et al.
NeuFlow v2: High-Efficiency Optical Flow Estimation on Edge Devices
by Zhiyong Zhang, Aniket Gupta, Huaizu Jiang, Hanumant Singh
First submitted to arxiv on: 19 Aug 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 A novel optical flow estimation method that balances high accuracy with reduced computational demands is proposed. Building upon NeuFlow v1, the approach introduces a lightweight backbone and fast refinement module to achieve state-of-the-art performance at a fraction of the computational cost. Compared to other state-of-the-art methods, this model achieves a 10x-70x speedup while maintaining comparable performance on synthetic and real-world data. The method is capable of running at over 20 FPS on 512×384 resolution images on a Jetson Orin Nano. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to estimate movement in videos is developed. This approach makes sure it’s both very accurate and doesn’t use too much computer power. It uses some existing ideas as a base, but adds new parts that make it faster and more efficient. This method can process video frames quickly, taking only about 1/20th of a second to do 512×384 resolution images on a special computer chip. |
Keywords
» Artificial intelligence » Optical flow
