@software{Gorordo_Fernandez_pyKinectAzure,
author = {Gorordo Fernandez, Ibai},
title = {{pyKinectAzure}}
}
@InProceedings{Huang_2020_CVPR,
author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
@article{huang2020aid,
title={AID: Pushing the Performance Boundary of Human Pose Estimation with Information Dropping Augmentation,
author={Huang, Junjie and Zhu, Zheng and Huang, Guan and Du, Dalong},
journal={arXiv preprint arXiv:2008.07139},
year={2020}
}
- This task use multiple azure kinects(abbreviated AK) to estimate human posture with its own SDK.
- AKs’ estimations are as observation for Belief Propagation(abbreviated BP).
- Using UDP-Pose's estimations to get more observation for BP.
- Realtime(in realtime.py) and offline(in dataProcess.py [def BPapplication]) mode are available.
- Not all defs are used, some defs was used for testing datas or something else.
- To be replenished.
data process:
1.run DataProcess.getAKintrisics() function get intrisics of AK. 2.run utils.calculateRTandSave() function get RT among AK. Chessboard is needed. 3.run RealtimeVariAK.esitBoneLength() function get bone length of a participant. 4.run RealtimeVariAK.start() function to obtain BP estimation.utils.LineScoreOfMultiAK() needs torch 1.12.x which has class FasterRCNN_ResNet50_FPN_Weights. It's correctness needs to be verified.