Person Detection for Social Distancing and Safety Violation Alert based on Segmented ROI

Person detection algorithm used is MobileNet SSD with Caffe implementation and the model pre-trained on MS-COCO. Both programs uses OpenCV for image processing and utilizing the DNN module (tested on CPU). The programs later tested on several datasets to prove the concepts.

1. Prerequisites and configurations

All the requirements can be installed via the command:

$ pip install -r requirements.txt

The default input is video located in videos file. To change the program to use camera stream as input, you need to change the configuration from CAMERA = False to CAMERA = True.

Note: All configurations can be changed in the config.py file.

2. Run project

For social distancing program, run:

$ python safety_violation_alert.py

For safety violation alert based on segmented ROI program, run:

$ python safety_violation_alert.py

3. Program output

Social distance monitoring	Safety violation alert based on segmented ROI

4. Accuracy for social distance monitoring

5. Accuracy for safety violation alert based on segmented ROI

6. References

Mobilenet SSD Caffe

Dataset

MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets




A Camera Network Tracking (CamNeT) Dataset and Performance Baseline

Publication

Person Detection for Social Distancing and Safety Violation Alert based on Segmented ROI

LICENSE

This project is licensed under the terms of the MIT license.