README.md

---
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](https://arxiv.org/abs/2309.09515) · [Project Page](https://lyhsieh.github.io/sphp/) · [Code](https://github.com/lyhsieh/SPHP) · [Video](https://youtu.be/BdwL34Bd7e8)

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](https://github.com/lyhsieh/SPHP).

## 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](https://forms.gle/wsfpLX6g7A1FDz5y5)).

- 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

```bibtex
@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}
}
```