Sold by: Providence HealthcareÂ
Deployed on AWS
This machine learning model uses computer vision to detect aggregate level of social distancing in waiting rooms within hospitals.
Overview
Video-based AI that detects when room occupancy or social distancing falls outside COVID-19 policy. Analyzes statistical patterns at scale.
See also separate Facemask compliance detection module.
Created by Providence Health Care, working with multidisciplinary teams from the University of British Columbia, including the Dept. of Computing Science & Computer Vision Lab. Our mission is to improve patient outcomes, particularly the most vulnerable in our Long-Term Care and Acute Care settings.
Highlights
- This machine learning models uses computer vision to detect aggregate level of social distancing in waiting rooms within hospitals. The model is currently in the pilot phase at St. Paul's Hospital in Vancouver, Canada in order to ensure physical distancing and compliance to infection control procedures for the safety of our patients, staff and visitors. The model utilizes Yolo-v3 objection detection model and weights by applying a single neural network to the full image. No personally identifiable information is stored as part of the analysis. This model is not intended for medical use.
- Using YOLO v3, the model uses a variant of Darknet, which originally has 53 layer network trained on Imagenet. For the task of detection, 53 more layers are stacked onto it, giving us a 106 layer fully convolutional underlying architecture for YOLO v3. YOLO v3 is a fully convolutional network and its eventual output is generated by applying a 1 x 1 kernel on a feature map. In YOLO v3, the detection is done by applying 1 x 1 detection kernels on feature maps of three different sizes at three different places in the network. YOLO v3 makes prediction at three scales, 32, 16 and 8 respectively.
- Utilizing Yolo-v3 object detection model, we apply a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for human class for each region. The distance between centroid of two detected human bounding boxes are measured, with the assumption that 150cm is X amount of pixels apart in the video. The number of social distancing violation and the total number of people is recorded to a CSV file for statistical analysis as aggregate statistics. No personally identifiable information (PII) is stored.
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m4.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.2xlarge instance type, real-time mode | $0.00 |
ml.m4.2xlarge Inference (Batch) Recommended | Model inference on the ml.m4.2xlarge instance type, batch mode | $0.00 |
ml.m4.4xlarge Inference (Real-Time) | Model inference on the ml.m4.4xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Inference (Real-Time) | Model inference on the ml.m5.4xlarge instance type, real-time mode | $0.00 |
ml.m4.16xlarge Inference (Real-Time) | Model inference on the ml.m4.16xlarge instance type, real-time mode | $0.00 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $0.00 |
ml.p3.16xlarge Inference (Real-Time) | Model inference on the ml.p3.16xlarge instance type, real-time mode | $0.00 |
ml.c5.2xlarge Inference (Real-Time) | Model inference on the ml.c5.2xlarge instance type, real-time mode | $0.00 |
ml.p3.2xlarge Inference (Real-Time) | Model inference on the ml.p3.2xlarge instance type, real-time mode | $0.00 |
ml.c4.2xlarge Inference (Real-Time) | Model inference on the ml.c4.2xlarge instance type, real-time mode | $0.00 |
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Delivery details
Amazon SageMaker model
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Version release notes
beta subject to validation and further training
Additional details
Inputs
- Summary
This model is absolutely not intended as a substitue for a doctor's medical diagnosis.
Input Supported content types: video
This model accepts videos in the mime-type specified above.
Output Content type: text/plain Please refer to the github notebook for the sample output.
Invoking Endpoint For a single image file: Create an endpoint and provide the source video file in the data directory. The output is obtained in the form of a text file output.txt in the data directory.
- Input MIME type
- text/csv
See Input Summary
See Input Summary
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Image classification model that detects violations of social distancing in public areas
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