Fall Detection Computer Vision Dataset

bytest1Task:
Object Detection
License:CC BY 4.0

About Fall Detection Dataset

Deploy a specialized fall detection model trained on a massive dataset of 4.5k real-world images. This project provides a pre-trained computer vision model ready for integration into elderly care and workplace safety systems, offering a reliable foundation for identifying fall incidents.

Ways to Use Fall Detection Model

  1. Elderly Care Monitoring: The Fall Detection model can be integrated into smart home systems or camera-assisted monitoring services to promptly identify when elderly individuals fall, enabling caregivers or family members to respond quickly to potential injuries or medical emergencies.

  2. Workplace Safety: In high-risk work environments like construction sites or factories, the Fall Detection model can be implemented to monitor employees and detect any accidents, alerting supervisors or emergency medical services immediately to provide assistance.

  3. Public Safety: Security cameras in public spaces such as parks, streets, or shopping centers can utilize the Fall Detection model to detect falls and possible criminal activities or accidents, allowing law enforcement or emergency services to respond in a timely manner.

  4. Assisted Living Facilities: The Fall Detection model can help improve the safety of residents in assisted living facilities, nursing homes, or rehabilitation centers by monitoring common areas for falls and automatically notifying staff members when incidents occur.

  5. Sports Injury Detection: The Fall Detection model can be used in gyms or sports centers to monitor athletes during training sessions, helping to quickly identify falls or injuries and enabling coaches or medical staff to intervene if necessary.

Use Free Fall-Detected Detection API

Powered by general detection model

Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "Fall-Detected"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 

Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ fall-detection-ca3o8-ndqlk_dataset,
  title = { Fall Detection Dataset },
  type = { Open Source Dataset },
  author = { test1 },
  howpublished = { \url{ https://universe.roboflow.com/test1-2zjd9/fall-detection-ca3o8-ndqlk } },
  url = { https://universe.roboflow.com/test1-2zjd9/fall-detection-ca3o8-ndqlk },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2026 },
  month = { jun },
  note = { visited on 2026-07-29 },
}

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