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Vehicle Detection And Counting Using OpenCv | Vehicle Counting using OpenCV | Python
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Introduction
I worked on a video-based vehicle counting system (VCS). I shared a demo on Instagram that went viral!
In this video, I’ll explain why and take you through how I built it, discussing how it works, how I learned the libraries used, the components of the system, algorithms and models I experimented with and the results obtained. Let’s get started!
I built a video-based vehicle counting system using Python/OpenCV. You can find the code on my Google Drive Link below.
Source Code & Link.
How it works
The vehicle counting system I built is made up of three main components: a detector, tracker and counter. The detector identifies vehicles in a given frame of video and returns a list of bounding boxes around the vehicles to the tracker. The tracker uses the bounding boxes to track the vehicles in subsequent frames. The detector is also used to update the trackers periodically to ensure that they are still tracking the vehicles correctly. The counter counts vehicles when they leave the frame or makes use of a counting line drawn across a road.
Why vehicle counting?
Computer Vision (CV) had been on my list of things to learn for a long time so I decided to use the opportunity of my final project to learn it. I actually wanted to build a turn-based or real-time strategy game that used a healthy dose of AI but I knew I wouldn’t have been able to complete it in time for my defence so I figured a CV project was the way to go as I’d very likely get to use Machine Learning (ML).
Computer Vision is an interdisciplinary field concerned with giving computers the ability to “see” or be able to understand the contents of digital images such as photos and videos. While vision is a trivial task for humans and animals, it’s currently quite difficult for machines. However, a lot of progress has been made in the field in the last few decades and new techniques and technologies to make CV faster and more accurate are actively being researched.
A vehicle counting system, as you might have already inferred, is a system that counts vehicles on the road. Why would you want to build one? Why would you want to count vehicles on the road? Here are some reasons:
Traffic management and planning
Traffic control
Parking management
Advertising
Why video?
There are a handful of ways to count vehicles on the road from manual counts to pneumatic tubes to piezoelectric sensors. Why was video used? Why is it preferred?
Sensor data (video footage) can be used to verify the system’s results which makes it easier and faster to evaluate and improve the system.
The footage can also be used for other purposes including surveillance, automatic plate number recognition, vehicle type detection and vehicle speed detection to name a few.
It is relatively cheaper to implement and scale as a permanent vehicle counting system compared to other systems.
It can track and count multiple vehicles moving in different directions across several lanes.
Algorithm Used here is BackgroundSubtractorMOG.
I will put one more video to explain the full code if you are getting any error while running this program please let me know in command section I will clear it.
Thank you Have a Good Day.
For more PYTHON videos:
1) Real-Time Face recognition
2) Smile Detector Using python
3)Face and Eye detection Using python
4)Fire detection using python.
5)Bird Detection Using python
6)Face recognition based attendance system
#Vehicledetection, #Python, #Vehicledetectionpython, #Vehicledetectionusingopencvpython, #Opencvpythontutorial, #Opencvpython, #Opencv, #Vehiclecountingopencvpython, #Vehiclecountingpython
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I worked on a video-based vehicle counting system (VCS). I shared a demo on Instagram that went viral!
In this video, I’ll explain why and take you through how I built it, discussing how it works, how I learned the libraries used, the components of the system, algorithms and models I experimented with and the results obtained. Let’s get started!
I built a video-based vehicle counting system using Python/OpenCV. You can find the code on my Google Drive Link below.
Source Code & Link.
How it works
The vehicle counting system I built is made up of three main components: a detector, tracker and counter. The detector identifies vehicles in a given frame of video and returns a list of bounding boxes around the vehicles to the tracker. The tracker uses the bounding boxes to track the vehicles in subsequent frames. The detector is also used to update the trackers periodically to ensure that they are still tracking the vehicles correctly. The counter counts vehicles when they leave the frame or makes use of a counting line drawn across a road.
Why vehicle counting?
Computer Vision (CV) had been on my list of things to learn for a long time so I decided to use the opportunity of my final project to learn it. I actually wanted to build a turn-based or real-time strategy game that used a healthy dose of AI but I knew I wouldn’t have been able to complete it in time for my defence so I figured a CV project was the way to go as I’d very likely get to use Machine Learning (ML).
Computer Vision is an interdisciplinary field concerned with giving computers the ability to “see” or be able to understand the contents of digital images such as photos and videos. While vision is a trivial task for humans and animals, it’s currently quite difficult for machines. However, a lot of progress has been made in the field in the last few decades and new techniques and technologies to make CV faster and more accurate are actively being researched.
A vehicle counting system, as you might have already inferred, is a system that counts vehicles on the road. Why would you want to build one? Why would you want to count vehicles on the road? Here are some reasons:
Traffic management and planning
Traffic control
Parking management
Advertising
Why video?
There are a handful of ways to count vehicles on the road from manual counts to pneumatic tubes to piezoelectric sensors. Why was video used? Why is it preferred?
Sensor data (video footage) can be used to verify the system’s results which makes it easier and faster to evaluate and improve the system.
The footage can also be used for other purposes including surveillance, automatic plate number recognition, vehicle type detection and vehicle speed detection to name a few.
It is relatively cheaper to implement and scale as a permanent vehicle counting system compared to other systems.
It can track and count multiple vehicles moving in different directions across several lanes.
Algorithm Used here is BackgroundSubtractorMOG.
I will put one more video to explain the full code if you are getting any error while running this program please let me know in command section I will clear it.
Thank you Have a Good Day.
For more PYTHON videos:
1) Real-Time Face recognition
2) Smile Detector Using python
3)Face and Eye detection Using python
4)Fire detection using python.
5)Bird Detection Using python
6)Face recognition based attendance system
#Vehicledetection, #Python, #Vehicledetectionpython, #Vehicledetectionusingopencvpython, #Opencvpythontutorial, #Opencvpython, #Opencv, #Vehiclecountingopencvpython, #Vehiclecountingpython
***
Follow Me Here For More Help or Queries
***
SUBSCRIBE for weekly videos on Programming Language, Technology, Science, Space, and Many More.
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