vehicle-counter—image-processing

Project Description

-> vehicle-counter is an image-processing based project made using made using python, OpenCV library.

-> This project counts vehicle passing through a particular point.
-> It automatically detects the vehicles (by marking it with a green rectangle and a red point at the centre) and counts while passing through a particular location.

Introduction-

I love the idea of smart cities. The thought of automated smart energy systems, electrical grids, one-touch access ports – it’s an enthralling concept! Honestly, it’s a dream for a data scientist and I’m delighted that a lot of cities around the world are moving towards becoming smarter.

One of the core components of a smart city is automated traffic management. And that got me thinking – could I use my data science chops to build a vehicle detection model that could play a part in smart traffic management?
Think about it – if you could integrate a vehicle detection system in a traffic light camera, you could easily track a number of useful things simultaneously:

It looks like

• How many vehicles are present at the traffic junction during the day?
  
• What time does the traffic build up?
  
• What kind of vehicles are traversing the junction (heavy vehicles, cars, etc.)?
• Is there a way to optimize the traffic and distribute it through a different street?
• And so on. The applications are endless!

We humans can easily detect and recognize objects from complex scenes in a flash. Translating that thought process to a machine, however, requires us to learn the art of object detection using computer vision algorithms.
So in this project, we will be building an automatic vehicle detector and counter model.

Tools and Languages:

Installing Libraries

pip install numpy
pip install opencv
pip install times

-> We will use numpy library because it contains a multi-dimensional array and matrix data structures. It can be utilised to perform a number of mathematical operations on arrays such as trigonometric, statistical, and algebraic routines.
-> We use OpenCV library because it is a cross-platform library using which we can develop real-time computer vision applications. It mainly focuses on image processing, video capture and analysis including features like face detection and object detection

Steps to follow

-Install the given libraries

-Download the code from the given github repository

-Run the code

steps followed while making this project:—-

1.Read the video frame by frame.

2.Apply some fileters to the frame(dilation, etc.).

3.Use BackgroundSubtractor to split the foreground from background(white-foreground, black-background).

4.Detect the contours of the foreground(moving objects).

5.Calculate the centroid of each moving object.

6.For each centroid, detect if there’s a nearby centroid of the last frame. If so, assign them to the same vehicle.

7.For each vehicle, detect whether it crossed the target line.

The Idea Behind Detecting Moving Objects in Videos—-

Object detection is a fascinating field in computer vision. It goes to a whole new level when we’re dealing with video data. The complexity rises up a notch, but so do the rewards!

We can perform super useful high-value tasks such as surveillance, traffic management, fighting crime, etc. using object detection algorithms

-> Here is the output of the project

Output

Developed by:

Sambit Kumar Tripathy