SchulzR97/TUC-HRI-CS

University of Technology Chemnitz, Germany
Department Robotics and Human Machine Interaction
Author: Robert Schulz

TUC-HRI Dataset Card

TUC-AR is an action recognition dataset, containing 10(+1) action categories for human machine interaction. This version contains video sequences, stored as images, frame by frame.

We introduce two validation types: random validation and cross-subject validation. This is the cross-subject validation dataset. For random validation, please use https://huggingface.co/datasets/SchulzR97/TUC-HRI.

• In random validation, a train and a validation split are obtained by randomly splitting the sequences while maintaining an allocation rate of approximately 80% train / 20% validation. This ensures that each action, subject, and camera, as well as the overall number of sequences, are distributed in this ratio among the splits. Thus, we obtained 17,263 train sequences and 4,220 validation sequences.
• For cross-subject validation, subject 0 and 8 were chosen as validation subjects. All other subjects were assigned to the train split.

Dataset Details

• RGB and depth input recorded by Intel RealSense D435 depth camera
• 12 subjects
• 11,031 sequences (train 8,893/ val 2,138)
• 3 perspectives per scene
• 10(+1) action classes

  Action	Label
  A000	None
  A001	Waving
  A002	Pointing
  A003	Clapping
  A004	Follow
  A005	Walking
  A006	Stop
  A007	Turn
  A008	Jumping
  A009	Come here
  A010	Calm

How to Use this Dataset

1. Install the RSProduction Machine Learning package (PyPi, GitHub)

pip install rsp-ml

2. Use the HF datasat with rsp.ml.dataset.TUCHRI

from rsp.ml.dataset import TUCHRI
import rsp.ml.multi_transforms as multi_transforms
import torchvision.transforms as transforms
USE_DEPTH_DATA = True
class ToNumpy:
  def __call__(self, x):
    if isinstance(x, Image.Image):
      return np.array(x)
    elif isinstance(x, torch.Tensor):
      return x.permute(1, 2, 0).numpy()  # Tensor (C, H, W) -> (H, W, C)
    else:
      raise TypeError("Input must be a PIL.Image or torch.Tensor")
transform = transforms.Compose([
  transforms.Resize((600, 600)),
  transforms.ColorJitter(brightness=0.8, contrast=0.8, saturation=0.8, hue=0.5),
  transforms.RandomRotation(180, expand=True),
  transforms.CenterCrop((375, 500)),
  #transforms.RandomCrop(input_size),
  #transforms.ToTensor(),
  ToNumpy()
])
dtd_dataset = torchvision.datasets.DTD(download=True, split='val', transform=transform)
tranforms_train = multi_transforms.Compose([
  multi_transforms.ReplaceBackground(
      backgrounds = backgrounds,
      hsv_filter=[(69, 87, 139, 255, 52, 255)],
      p = 0.8
  ),
  multi_transforms.Resize((400, 400), auto_crop=False),
  multi_transforms.Color(0.1, p = 0.2),
  multi_transforms.Brightness(0.7, 1.3),
  multi_transforms.Satturation(0.7, 1.3),
  multi_transforms.RandomHorizontalFlip(),
  multi_transforms.GaussianNoise(0.002),
  multi_transforms.Rotate(max_angle=3),
  multi_transforms.Stack()
])
transforms_val = multi_transforms.Compose([
  multi_transforms.Resize((400, 400), auto_crop=False),
  multi_transforms.Stack()
])
ds_train = TUCHRI(
  phase='train',
  load_depth_data=True,
  sequence_length=30,
  num_classes=11,
  transforms=tranforms_train
)
ds_val = TUCHRI(
  phase='val',
  load_depth_data=True,
  sequence_length=30,
  num_classes=11,
  transforms=transforms_val
)

Dataset Card Contact

In case of any doubts about the dataset preprocessing and preparation, please contact TUC RHMi.