Summary of Kinect Calibration and Data Optimization For Anthropometric Parameters, by M.s. Gokmen et al.
Kinect Calibration and Data Optimization For Anthropometric Parameters
by M.S. Gokmen, M. Akbaba, O. Findik
First submitted to arxiv on: 13 Sep 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 This paper explores the challenges of using Microsoft Kinect sensors for 3D vision systems in medical and biometric applications. The Kinect sensor’s ability to capture depth images and 3D coordinates of human joints enables the extraction of anthropometric features, but the raw data is unstable due to varying distances between joints and kinect sensor locations. To address this issue, a novel method is proposed for calibrating the kinect sensor and optimizing skeleton features. Experimental results demonstrate the effectiveness of this approach, suggesting its potential for further study in broader scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about using special cameras called Kinect sensors to make 3D images of people’s bodies. These cameras are really useful for things like medicine and security checks. The problem is that the data they collect can be messy because it depends on how far apart the person’s joints are, and where the camera is pointing. To fix this, scientists came up with a new way to make sure the camera is working correctly and the body measurements are accurate. They tested their method and found out it works really well! |
