Skip to content

Latest commit

 

History

History
78 lines (55 loc) · 3.75 KB

README.md

File metadata and controls

78 lines (55 loc) · 3.75 KB
Raw

Social Distancing AI

Tool to monitor social distancing from CCTV, videos using Python, Deep learning, Computer Vision. This tool can automatically estimate interpersonal distance from uncalibrated RGB cameras. Can be used at public places and workplace.

In the fight against the COVID-19, social distancing has proven to be a very effective measure. To ensure social distancing protocol in public places and workplace, I have developed social distancing detection tool that can monitor if people are keeping a safe distance from each other by analyzing real time video streams from the camera.

This tool has following features:

  • Detect humans in the frame with yolov3.
  • Calculates the distance between every human who is detected in the frame.
  • Shows how many people are at High, Low and Not at risk.

Requirements:

You will need the following to run this code:
Python 3.5.2
Opencv(CV2) 4.2.0
numpy 1.14.5
argparse

For human detection:
yolov3.weights, yolov3.cfg files

For running: 
Good GPU, for faster results. CPU is also fine(I have tried it on CPU).

File Structure:

main.py     : Detects and calculates distance between humans
utills.py   : Contain functions to calculate distance, scale, transformed points
plot.py     : Contain functions to draw bird eye view and frame
models      : Contain yolo weights and cfg.
data        : Contain video sample

Usage:

 * If following same directory structure   
     python main.py
 * If paths for models, input video is different then given directory structure
     python main.py --model='model path' --video_path='path to video file' --output_dir='output directory' --output_vid='output vid directory'

How it works:

 * Run following command(if directory structure is same) 
     python main.py
     
 * You will get a frame where you can draw ROI and distance scale. It will take 8 points on first frame using mouse click 
   event. First four points will define ROI where we want to monitor social distancing. Also these points should form 
   parallel lines in real world if seen from above(birds eye view). Next 3 points will define 6 feet(unit length) 
   distance in horizontal and vertical direction and those should form parallel lines with ROI. Unit length we can take 
   based on choice. Points should pe in pre-defined order - bottom-left, bottom-right, top-right, top-left, point 5 and 6 
   should form horizontal line and point 5 and 7 should form verticle line. Horizontal and vertical scale will be
   different. Gif below will help understand points better.
   
 * We will transform prespective of ROI so that we can have top view of scene or ROI. This top view or bird eye view has 
   the property that points are distributed uniformally horizontally and vertically(scale for horizontal and vertical 
   direction will be different). So for bird eye view points are equally distributed, which was not case for normal view.
   
 * YOLO V3 is used to detect humans in frame and by calculating bottom center point of bounding boxe around humans, 
   we transform those points to bird eye view. And then calculates risk factor by calculating distance between
   points and then drawing birds eye view and drawing bounding boxes and distance lines between boxes on frame.
   
 * Distance calculation works best for ROI.

Bird Eye View

Output:

Bird's Eye View
Bird Eye View

Output frames

More result frames are in output folder