Datasets:
pretty_name: 'SPHP: Sparse and Privacy-enhanced Representation for Human Pose Estimation'
license: other
license_name: sphp-data-agreement
license_link: https://forms.gle/wsfpLX6g7A1FDz5y5
task_categories:
- keypoint-detection
tags:
- human-pose-estimation
- motion-vector
- edge-image
- privacy-enhanced
- sparse-representation
- bmvc2023
size_categories:
- 100K<n<1M
extra_gated_heading: Request access to the SPHP dataset
extra_gated_prompt: >-
SPHP is collected with a proprietary Novatek Motion Vector Sensor and involves
human participants. Access is granted for non-commercial academic research
only. You must not redistribute the raw data, and you agree to the SPHP
data-use agreement. Requests are reviewed and approved manually by the authors
/ lab.
extra_gated_fields:
Full name: text
Affiliation / Institution: text
Email: text
Intended research use: text
I will use the data for non-commercial academic research only: checkbox
I will not redistribute the raw data without authorization: checkbox
extra_gated_button_content: Request access
SPHP: Sparse and Privacy-enhanced Representation for Human Pose Estimation
BMVC 2023 · Paper · Project Page · Code · Video
Ting-Ying Lin*, Lin-Yung Hsieh*, Fu-En Wang, Wen-Shen Wuen, Min Sun — National Tsing Hua University · Novatek Microelectronics Corp.
Dataset summary
SPHP is an in-house dataset for Human Pose Estimation (HPE) built from a proprietary Motion Vector Sensor (MVS). Instead of RGB video, each frame is represented by a sparse, privacy-enhanced pair of an edge image and a two-directional motion-vector image, together with ground-truth labels for 13 body joints and corresponding grayscale images.
- 40 participants (20 male, 20 female)
- 16 fitness-related actions, grouped into 4 classes (C1 upper-body, C2 lower-body, C3 slow whole-body, C4 fast whole-body)
- Modalities per frame: GR (grayscale), EDG (edge), MVH/MVV (horizontal/vertical motion vectors), plus pose_change ground-truth joint labels
Files
| File | Size | Notes |
|---|---|---|
Master.tar.gz |
~27 GB | Master-view captures |
Slave.tar.gz |
~28 GB | Slave-view captures (same structure as Master) |
After extraction (tar zxvf), the structure is:
data/
├── calibrate.npy
├── Master/
│ └── sXX/ # subject id
│ └── <action_id>/ # 01 .. 16
│ ├── EDG/ # 300 png (edge images)
│ ├── MVH/ # 300 png (horizontal motion vector)
│ ├── MVV/ # 300 png (vertical motion vector)
│ └── pose_change/ # 300 npy (ground-truth joint labels)
└── Slave/ # same structure as Master
Training/testing subject splits and full usage are documented in the official code repository.
Intended use
Research on efficient / privacy-preserving human pose estimation, sparse representations, and sparse-convolution models.
⚠️ License & access terms
This dataset is not released under a standard open license. It was collected with a proprietary, patent-pending Motion Vector Sensor from Novatek Microelectronics, and the original distribution requires signing a data-use agreement (via the project's Google Form).
- Use is limited to non-commercial academic research.
- Redistribution of the raw data is not permitted without authorization from the authors and Novatek.
- Human-subject data: although the representation is privacy-enhanced (edge / motion vectors, not raw video), please respect the participants' consent terms.
If you need access, please contact the authors / lab and complete the required agreement.
Citation
@inproceedings{lin2023sparse,
title = {Sparse and Privacy-enhanced Representation for Human Pose Estimation},
author = {Lin, Ting-Ying and Hsieh, Lin-Yung and Wang, Fu-En and Wuen, Wen-Shen and Sun, Min},
booktitle = {British Machine Vision Conference (BMVC)},
year = {2023}
}