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guiggh / hand_pose_action

Dataset and code for the paper "First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations", CVPR 2018.
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action-recognition benchmark computer-vision dataset hand-pose-estimation

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First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations

This repository contains instructions on getting the data and code of the work First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations presented at CVPR 2018. For more information on the benchmark please check out [1].

Downloading the data

Please fill this form to download the dataset after reading the terms and conditions.

Dataset structure:

The dataset is organized as the following example:

Comment: Check Figure 3 and 4 of the paper to learn about action categories. We used a slightly different nomenclature for some actions compared to the paper. These are: "dish soap -> liquid soap"; "read paper -> read letter"; "use spray -> use flash". Note: Check Subjects_info folder for details on number of sequences, frames, etc. for each subject. The following sequences can be ignored (they were not used in the paper): 'Subject_2/close_milk/4', 'Subject_2/put_tea_bag/2' and 'Subject_4/flip_sponge/2'.

Image data details

Hand pose data:

Format of each line of skeleton.txt: t x_1 y_1 z_1 x_2 y_2 z_2 ... x_21 y_21 z_21

where t is the frame number and x_i y_i z_i are the world coordinates (in mm) of joint i at frame t.

Hand joints are organised as follows: [Wrist, TMCP, IMCP, MMCP, RMCP, PMCP, TPIP, TDIP, TTIP, IPIP, IDIP, ITIP, MPIP, MDIP, MTIP, RPIP, RDIP, RTIP, PPIP, PDIP, PTIP], where ’T’, ’I’, ’M’, ’R’, ’P’ denote ’Thumb’, ’Index’, ’Middle’, ’Ring’, ’Pinky’ fingers.

hand_model

Check out the scripts load_example.x (.py for Python and .m for Matlab) for examples on how to visualise the hand pose on both color and depth images.

Updated 20/02/2019: We also provide action sequences with normalized hand poses. Normalization of hand poses is essential to replicate the action recognition results on the paper. It's briefly mentioned on the paper, but if you want to normalize the hand poses you will need to: compute average distance among subjects between joints, normalize the distance between joints to have the same distance on every frame and subject, make the wrist the origin of coordinates for each frame and (optional but helps) align the wrist with one of the axis by rotating the 3D skeleton.

Object pose data:

Available objects: 'juice carton', 'milk bottle', 'salt' and 'liquid soap'. Format of each line of object_pose.txt:

t M11 M21 M31 M41 M12 ... Mij... M44

where Mij is the element of the transformation matrix M at row i and column j.

Check the Python code load_example.py to see an example on how to visualise the object model for a given pose on top of the image.

Object models

Available objects: 'juice carton', 'milk bottle', 'salt' and 'liquid soap'.

Format .PLY. Each object comes with a texture file texture.jpg. Coordinates are in meters (in contrast to mm for hand poses).

Juice carton and milk bottle objects also appear in this popular 6D object pose estimation dataset and part of the recent 6D ECCV 2018 benchmark. We recaptured the object models attempting to improve the quality. Feel free to use the older models, however our object pose data is annotated for the new models.

Comment: The milk bottle model is not exactly the same as the one used when capturing the dataset. The object got lost (campus cleaning services) and when we bought the milk model again the brand had (slightly) changed the bottle design.

Camera parameters:

Depth sensor (intrinsics)

Image center:

Focal Length:

RGB sensor (intrinsics)

Image center:

Focal Length:

Extrinsics

R = [0.999988496304, -0.00468848412856, 0.000982563360594; 0.00469115935266, 0.999985218048, -0.00273845880292; -0.000969709653873, 0.00274303671904, 0.99999576807; 0,0,0];

t = [25.7; 1.22; 3.902; 1];

Benchmark tasks

In this section we describe the protocols used for the experiments on the paper.

Action recognition

data_split_action_recognition.txt contains the 1:1 split reported on the paper. These are the files you should use for training and testing if you want to compare with the results reported.

Hand pose estimation

Terms and conditions

The download and use of the dataset is released for academic research only and it is free to researchers from educational or research institutes for non-commercial purposes. When downloading the dataset you agree to (unless with expressed permission of the authors): not redistribute, modificate, or commercial usage of this dataset in any way or form, either partially or entirely.

If using this dataset, please cite the following paper:

@inproceedings{FirstPersonAction_CVPR2018,
  title={First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations},
  author={Garcia-Hernando, Guillermo and Yuan, Shanxin and Baek, Seungryul and Kim, Tae-Kyun}
  booktitle = {Proceedings of Computer Vision and Pattern Recognition ({CVPR})},
  year = {2018}
}

Acknowledgments

This dataset is part of Imperial College London-Samsung Research project, supported by Samsung Electronics.

Authors thank Gabriel Garcia for object model acquisition and Yana Hasson for providing Python scripts and feedback on the dataset.

References

[1] First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations, Guillermo Garcia-Hernando, Shanxin Yuan, Seungryul Baek and Tae-Kyun Kim, CVPR 2018. arXiv