Skip to content

Latest commit

 

History

History
35 lines (25 loc) · 1.92 KB

README.md

File metadata and controls

35 lines (25 loc) · 1.92 KB
Raw

Overview

I already have a repo for car counting and tracking but here I decided to detect car stop.

So this repo is based on CarCounterYOLOv3 with some changes in it's structure and code.

At the very beginning a thought that it was enough just to check if centroid coords on N and N+1 frames are the same. That would indicate that a car is standing still.

BUT

YOLOv3 works not perfectly and even if I can see that the car is not moving it's centroid is "shaking" on the video. That means that it's coords won't be exactly the same of two different frames.

So I decided to consider a car not moving if it's centroid's coords are SLIGHTLY changing but are still not very different on separate frames.

So how it works:

  • If distance between car centroid's coords on 1 and 2 frames is shorter than a minimum (we can change it) then we put this centroid in a dictionary.
  • Dictionary looks like this ID (key) -> Number of frames on which distance that I was talking about above is "kept"(value)
  • If on the 3rd frame situation is the same (distance is small) than the number of frames increases. And so on...
  • If this continues for some time (or some amount of frames) then we can tell that the car is stopped.
  • If after decreasing the distance starts increasing than we can tell that the car is moving again.

(for more detailed explanation see comments in code)

How to run it:

  • Clone/Download this project.
  • Download YOLOv3 .weights here and put it to /yolo folder
  • Get all necessary modules via `pip install -r 'requirements.txt'.
  • Go to the directory with this project.
  • Download some videos of cars driving aroud o just use a test video01.mp4 that I've uploaded.
  • Type python car_stop_detector.py -y yolo --input videos/'PATH_TO_YOUR_VIDEO'.mp4 --output output --skip-frames 5 and hit Enter.
  • Enjoy!