metadata

language:
  - en
license: cc-by-nc-nd-4.0
task_categories:
  - image-to-image
  - object-detection
tags:
  - code
  - medical
dataset_info:
  - config_name: video_01
    features:
      - name: id
        dtype: int32
      - name: name
        dtype: string
      - name: image
        dtype: image
      - name: mask
        dtype: image
      - name: shapes
        sequence:
          - name: track_id
            dtype: uint32
          - name: label
            dtype:
              class_label:
                names:
                  '0': nurse
                  '1': doctor
                  '2': other_people
          - name: type
            dtype: string
          - name: points
            sequence:
              sequence: float32
          - name: rotation
            dtype: float32
          - name: occluded
            dtype: uint8
          - name: attributes
            sequence:
              - name: name
                dtype: string
              - name: text
                dtype: string
    splits:
      - name: train
        num_bytes: 27856
        num_examples: 64
    download_size: 23409734
    dataset_size: 27856
  - config_name: video_02
    features:
      - name: id
        dtype: int32
      - name: name
        dtype: string
      - name: image
        dtype: image
      - name: mask
        dtype: image
      - name: shapes
        sequence:
          - name: track_id
            dtype: uint32
          - name: label
            dtype:
              class_label:
                names:
                  '0': nurse
                  '1': doctor
                  '2': other_people
          - name: type
            dtype: string
          - name: points
            sequence:
              sequence: float32
          - name: rotation
            dtype: float32
          - name: occluded
            dtype: uint8
          - name: attributes
            sequence:
              - name: name
                dtype: string
              - name: text
                dtype: string
    splits:
      - name: train
        num_bytes: 37214
        num_examples: 73
    download_size: 28155019
    dataset_size: 37214

Medical Staff People Tracking - Object Detection dataset

The dataset contains a collection of frames extracted from videos captured within a hospital environment. The bounding boxes are drawn around the doctors, nurses, and other people who appear in the video footage.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on TrainingData to buy the dataset

The dataset can be used for computer vision in healthcare settings and the development of systems that monitor medical staff activities, patient flow, analyze wait times, and assess the efficiency of hospital processes.

Dataset structure

The dataset consists of 2 folders with frames from the video from a hospital. Each folder includes:

images: folder with original frames from the video,
boxes: visualized data labeling for the images in the previous folder,
.csv file: file with id and path of each frame in the "images" folder,
annotations.xml: contains coordinates of the bounding boxes, created for the original frames

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset

Data Format

Each frame from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the bounding boxes for people tracking. For each point, the x and y coordinates are provided.

Classes:

doctor - doctor in the frame
nurse - nurse in the frame
others - other people (not medical staff)

Example of the XML-file

Object tracking might be made in accordance with your requirements.

TrainingData provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets

TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets

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