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!

carstopdetection's People