Summary of Location-guided Head Pose Estimation For Fisheye Image, by Bing Li et al.
Location-guided Head Pose Estimation for Fisheye Image
by Bing Li, Dong Zhang, Cheng Huang, Yun Xian, Ming Li, Dah-Jye Lee
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 approach presents a novel method for estimating head pose using convolutional neural networks (CNNs) that directly tackles fisheye distortion in images. By leveraging knowledge of head location, the model reduces the negative impact of distortion on existing pose estimation models trained on undistorted images. A multi-task learning framework is developed to estimate both head pose and location simultaneously. The proposed network outperforms state-of-the-art one-stage and two-stage methods on fisheye-distorted versions of popular datasets, including BIWI, 300W-LP, and AFLW2000. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to better estimate the direction people are looking by using special cameras with a wide-angle view. These cameras have a problem called “fisheye distortion” that makes it hard for computers to accurately determine the direction someone is facing. The researchers developed a new way to use computer vision and machine learning to solve this problem, creating a special kind of neural network that can work directly from these distorted images. They also created some new datasets with these distorted images to test their approach. |
Keywords
» Artificial intelligence » Machine learning » Multi task » Neural network » Pose estimation
