- Many technical improvements have recently been made in the field of road safety, as accidents have been increasing at an alarming rate, and one of the major causes of such accidents is a driver's lack of attention. To lower the incidence of accidents and keep safe, technological breakthroughs should be made. One method is to use Lane Detection Systems, which function by recognizing lane borders on the road and alerting the driver if he switches to an incorrect lane marking. A lane detection system is an important part of many technologically advanced transportation systems. Although it is a difficult goal to fulfil because to the varying road conditions that a person encounters, particularly while driving at night or in daytime. A camera positioned on the front of the car catches the view of the road and detects lane boundaries. The method utilized in this research divides the video image into a series of sub-images and generates image-features for each of them, which are then used to recognize the lanes on the road.
For running the code, make sure that the following are installed on your local device.
| Requirements |
|---|
| Python 3.11.x |
| OpenCV |
| Tkinter |
| NumPy |
| Matplotlib |
| Seaborn |
- Clone this repo.
-
git clone https://github.com/karthikeyakondabathula/LaneSense
- Install the required libraries from Requirements
- Run LaneSense.py
1. Fill the Required Details and select the video footage
2. Real-Time Tracking of Lane
3. Results
