Note: The repo contains the dataset used in the paper, including Campus, Shelf, StoreLayout1, StoreLayout2. Along with the data, we provide some scripts to visualize the data, in both 2D and 3D, and also to evaluate with the results. The source code is not included as this is a commercial project, find more in http://aifi.io if you are interested.
Here we provide four datasets, including
- Campus: https://www.epfl.ch/labs/cvlab/data/data-pom-index-php/
- Shelf: http://campar.in.tum.de/Chair/MultiHumanPose
- StoreLayout1: proposed by AiFi Inc.
- StoreLayout2: proposed by AiFi Inc.
For convenient, you can find and download them by one click from OneDrive.
For each dataset, the structure of the directory is organized as follow
Campus_Seq1
├── annotation_2d.json
├── annotation_3d.json
├── calibration.json
├── detection.json
├── frames
│ ├── Camera0
│ ├── Camera1
│ └── Camera2
│ ├── 0060.720.jpg
│ ├── 0060.760.jpg
│ ├── 0060.800.jpg
│ └── xxxxxxxx.jpg
└── result_3d.json
The annotations were only provided in Campus and Shelf datasets
and the detection is generated using
Cascaded Pyramid Network (CPN) in https://github.com/zju3dv/mvpose.
The frames are renamed using timestamps, i.e. the name
of each file is the tiemstamp in second of that frame.
2D (2D annotation and detection) and 3D (3D annotation and tracking result) data have their own unified data format as follows.
The 2D data is organized by frames:
{
"image_wh": [360, 288],
"frames": {
"Camera0/0002.320.jpg": {
"camera": "Camera0",
"timestamp": 2.32,
"poses": []
},
"Camera0/0002.360.jpg": {
"camera": "Camera0",
"timestamp": 2.36,
"poses": [
{
"id": -1,
"points_2d": Nx2 Array,
"scores": N Array
},
...
]
},
...
}
}The 3D data is organized by timestamps:
[
{
"timestamp": 6.08,
"poses": [
{
"id": 10159970873491820000,
"points_3d": Nx3 Array,
"scores": N Array
},
...
]
},
...
]In the annotation the human pose has 14 keypoints:
0: 'r-ankle',
1: 'r-knee',
2: 'r-hip',
3: 'l-hip',
4: 'l-knee',
5: 'l-ankle',
6: 'r-wrist',
7: 'r-elbow',
8: 'r-shoulder',
9: 'l-shoulder',
10: 'l-elbow',
11: 'l-wrist',
12: 'bottom-head',
13: 'top-head'In detection and result, the human pose has 17 keypoints:
0: 'nose',
1: 'l-eye',
2: 'r-eye',
3: 'l-ear',
4: 'r-ear',
5: 'l-shoulder',
6: 'r-shoulder',
7: 'l-elbow',
8: 'r-elbowr',
9: 'l-wrist',
10: 'r-wrist',
11: 'l-hip',
12: 'r-hip',
13: 'l-knee'
14: 'r-knee'
15: 'l-ankle'
16: 'r-ankle'Along with the data, here we provide some tools to load the data and calibration, visualize and evaluate the result.
DATA_ROOT=/data/3DPose_pub/Campus_Seq1
# 2D
python display.py --frame-root ${DATA_ROOT}/frames --calibration ${DATA_ROOT}/calibration.json --pose-file ${DATA_ROOT}/annotation_2d.json --pose-type 2d
# 3D (only tested on Linux)
python display.py --frame-root ${DATA_ROOT}/frames --calibration ${DATA_ROOT}/calibration.json --pose-file ${DATA_ROOT}/annotation_3d.json --pose-type 3dDATA_ROOT=/data/3DPose_pub/Campus_Seq1
# 2D detection
python display.py --frame-root ${DATA_ROOT}/frames --calibration ${DATA_ROOT}/calibration.json --pose-file ${DATA_ROOT}/detection.json --pose-type 2d
# 3D result
python display.py --frame-root ${DATA_ROOT}/frames --calibration ${DATA_ROOT}/calibration.json --pose-file ${DATA_ROOT}/result_3d.json --pose-type 3dSometimes it's hard to setup the environment for vispy. Here we provide a dockerfile supports OpenGL and CUDA applications (from https://medium.com/@benjamin.botto/opengl-and-cuda-applications-in-docker-af0eece000f1).
-
To use it you will need
nvidia-container-runtime: https://github.com/NVIDIA/nvidia-container-runtime#installation -
Build the docker image
docker build -t glvnd-x-vispy:latest . -
Start the container
# Connecting to the Host’s X Server xhost +local:root docker run \ --rm \ -it \ --gpus all \ -v /tmp/.X11-unix:/tmp/.X11-unix \ -e DISPLAY=$DISPLAY \ -e QT_X11_NO_MITSHM=1 \ -v /PATH-TO-DATA/3DPose_pub:/data/3DPose_pub \ -v /PATH-TO-CODE/crossview_3d_pose_tracking:/app \ glvnd-x-vispy bash
-
Run the demo in a docker container
cd /app pip3 install -r requirements.txt DATA_ROOT=/data/3DPose_pub/Campus_Seq1 # 2D detection python3 display.py --frame-root ${DATA_ROOT}/frames --calibration ${DATA_ROOT}/calibration.json --pose-file ${DATA_ROOT}/detection.json --pose-type 2d # 3D result python3 display.py --frame-root ${DATA_ROOT}/frames --calibration ${DATA_ROOT}/calibration.json --pose-file ${DATA_ROOT}/result_3d.json --pose-type 3d
DATA_ROOT=/data/3DPose_pub/Campus_Seq1
python evaluate.py --annotation ${DATA_ROOT}/annotation_3d.json --result ${DATA_ROOT}/result_3d.json Then you will get the the output like
+------------+---------+---------+---------+---------+
| Bone Group | Actor 0 | Actor 1 | Actor 2 | Average |
+------------+---------+---------+---------+---------+
| Head | 1.0000 | 1.0000 | 0.9928 | 0.9976 |
| Torso | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
| Upper arms | 0.9592 | 1.0000 | 1.0000 | 0.9864 |
| Lower arms | 0.8980 | 0.7063 | 0.9348 | 0.8464 |
| Upper legs | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
| Lower legs | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
| Total | 0.9714 | 0.9413 | 0.9862 | 0.9663 |
+------------+---------+---------+---------+---------+
@InProceedings{Chen_2020_CVPR,
author = {Chen, Long and Ai, Haizhou and Chen, Rui and Zhuang, Zijie and Liu, Shuang},
title = {Cross-View Tracking for Multi-Human 3D Pose Estimation at Over 100 FPS},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}