Note

This is a Hugging Face dataset. For large datasets, ensure huggingface_hub>=1.1.3 to avoid rate limits. Learn more in the Hugging Face integration docs.

Hugging Face

Dataset Card for safe_unsafe_behaviours#

image/png

This is a FiftyOne dataset with 691 samples.

Installation#

If you haven’t already, install FiftyOne:

pip install -U fiftyone

Usage#

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/Safe_and_Unsafe_Behaviours")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details#

Dataset Description#

A high-resolution video dataset of safe and unsafe workplace behaviors collected from security cameras at a production facility, designed for occupational accident prevention research. The dataset contains 691 video clips capturing 8 behavior classes (4 safe, 4 unsafe) that represent common safety compliance scenarios in industrial environments including walkway violations, unauthorized equipment interventions, panel cover states, and forklift load compliance.

  • Curated by: OÄŸuzhan Önal and Emre Dandıl, Department of Computer Engineering, Faculty of Engineering, Bilecik Åžeyh Edebali University, Bilecik, Turkey

  • Shared by: KafaoÄŸlu Metal Plastik Makine San. ve Tic. A.Åž., EskiÅŸehir, Turkey

  • Language(s) (NLP): en

  • License: CC BY 4.0

Dataset Sources#

  • Repository: https://data.mendeley.com/datasets/xjmtb22pff/1

  • Paper: https://www.sciencedirect.com/science/article/pii/S235234092400756X

Uses#

Direct Use#

  • Video classification for industrial safety monitoring systems

  • Action recognition and temporal behavior detection research

  • Training real-time unsafe behavior detection models

  • Benchmarking video understanding models on industrial surveillance footage

  • Computer vision research for occupational health and safety applications

Out-of-Scope Use#

  • Deployment in environments significantly different from industrial manufacturing settings

  • Worker surveillance or performance monitoring without proper consent and ethical oversight

  • Applications requiring detection of safety behaviors not represented in the 8 defined classes

Dataset Structure#

This dataset is formatted for FiftyOne, an open-source tool for building high-quality datasets and computer vision models.

Dataset Info:

  • Name: safe_unsafe_behaviours

  • Media type: video

  • Num samples: 691

  • Splits: train (566 samples), test (125 samples) — indicated via sample tags

Sample Fields:

Field

Type

Description

id

ObjectIdField

Unique sample identifier

filepath

StringField

Path to video file

tags

ListField(StringField)

Split tags: train or test

metadata

EmbeddedDocumentField(VideoMetadata)

Video metadata (resolution, duration, fps, etc.)

ground_truth

EmbeddedDocumentField(Classification)

Video-level behavior classification label

Frame Fields:

Field

Type

Description

id

ObjectIdField

Unique frame identifier

frame_number

FrameNumberField

Frame index within video

Classes (8 total):

Class

Behavior Type

Description

Safe Walkway Violation

Unsafe

Worker goes beyond designated safe walkway boundaries

Unauthorized Intervention

Unsafe

Worker intervenes on equipment without proper safety gear/authorization

Opened Panel Cover

Unsafe

Panel cover left open after intervention

Carrying Overload with Forklift

Unsafe

Forklift carrying 3+ blocks

Safe Walkway

Safe

Worker stays within designated walkway

Authorized Intervention

Safe

Worker properly equipped for equipment intervention

Closed Panel Cover

Safe

Panel cover properly closed

Safe Carrying

Safe

Forklift carrying 2 or fewer blocks

Video Specifications:

  • Resolution: 1920×1080 pixels

  • Frame rate: 24 fps

  • Format: MP4

  • Duration: 1–20 seconds per clip

Dataset Creation#

Curation Rationale#

Unsafe behavior is a leading cause of workplace injuries and deaths. Despite regular safety inspections, accidents occur due to breaches of occupational health and safety protocols. This dataset was created to support the development of computer vision systems capable of real-time detection of unsafe behaviors before accidents occur, addressing the need for automated, continuous safety monitoring in industrial environments.

Source Data#

Data Collection and Processing#

Video footage was collected from security cameras at Kafaoğlu Metal Plastik Makine San. ve Tic. A.Ş., a production facility in an organized industrial zone in Eskişehir, Turkey. Collection occurred between November 5, 2022 and December 13, 2022 (39 days) using two different IP cameras. After collection, domain experts reviewed the footage to identify segments containing the defined safe and unsafe behaviors, extracting clips of 1–20 seconds containing the target behaviors.

Who are the source data producers?#

Workers and employees at KafaoÄŸlu Metal Plastik Makine San. ve Tic. A.Åž. performing normal production activities. Necessary permissions were obtained from company officials and employees prior to data collection.

Annotations#

Annotation process#

After videos were collected, frames containing safe and unsafe behaviors were identified by domain experts including factory managers and occupational safety specialists. Video clips were then extracted from the full surveillance footage. Some videos contain a single behavior class while others may contain multiple behavior classes.

Who are the annotators?#

Domain experts including factory managers and the occupational safety specialist at the facility where the videos were collected, in collaboration with the research team.

Personal and Sensitive Information#

The dataset contains video footage of workers performing their duties in an industrial setting. Workers’ faces and bodies are visible in the footage. Proper permissions were obtained from company officials and employees for the use of video recordings and images in academic studies. The permission documentation is maintained at the Department of Computer Engineering, Faculty of Engineering, Bilecik Şeyh Edebali University.

Bias, Risks, and Limitations#

  • Single environment: Data collected from one facility in Turkey, which may limit generalization to other industrial settings, equipment configurations, or geographic contexts

  • Temporal scope: 39-day collection period may not capture seasonal variations in behavior or clothing

  • Class definitions: Safety behaviors are specific to this facility’s protocols and may not align with regulations in other jurisdictions

  • Scale: 691 videos is relatively small for training deep learning models; data augmentation may be necessary

  • Camera perspectives: Only two camera viewpoints represented

Recommendations#

Users should consider domain adaptation techniques when applying models trained on this dataset to different industrial environments. The dataset is best suited for research and prototyping rather than direct production deployment without additional validation on target environments.

Citation#

BibTeX:

@article{ONAL2024110756,
  title = {Video dataset for the detection of safe and unsafe behaviours in workplaces},
  journal = {Data in Brief},
  volume = {56},
  pages = {110756},
  year = {2024},
  issn = {2352-3409},
  doi = {https://doi.org/10.1016/j.dib.2024.110756},
  url = {https://www.sciencedirect.com/science/article/pii/S235234092400756X},
  author = {Oğuzhan Önal and Emre Dandıl}
}

APA:

Önal, O., & Dandıl, E. (2024). Video dataset for the detection of safe and unsafe behaviours in workplaces. Data in Brief, 56, 110756. https://doi.org/10.1016/j.dib.2024.110756