We are making a project that would help the traffic system to detect cars that go wrong way and put theirs and other?s lives in danger. This project will take footage from Cameras to detect the vehicles that are going wrong way. The system will track the vehicle?s motion. This will help in gathering
Detection and Alert System of Wrong Way Vehicles
We are making a project that would help the traffic system to detect cars that go wrong way and put theirs and other’s lives in danger. This project will take footage from Cameras to detect the vehicles that are going wrong way. The system will track the vehicle’s motion. This will help in gathering the details of the driver and the evidence to take legal actions against the accuse. An immediate detection of a vehicle driving on the wrong direction could prevent serious accidents by warning the oncoming vehicles. As Archimedes once said, “Man has always learned from the past. After all, you can’t learn history in reverse!” Thus, considering this, we have come up with a project that would use data that has been fed into it to detect any vehicle that is going wrong way.
Our project aims the real time automatic wrong way vehicle detection and will generate alert sign on screen. This intelligence transport detection system to control road anomaly to detect the wrong way vehicle and extract the vehicle from its background, and then tracking all vehicles and verify if the direction of its route is the right way or wrote way classification. Moreover, this project will be more efficient and work more accurately as compared to other detection systems

Our wrong way detection system will provide the following features:
Our research will create a standardized detecting mechanism that will detect wrong-way drivers on the road. The system works in three stages: first, it detects vehicles from camera footage using the You Only Look Once (YOLO) algorithm, then it tracks each vehicle, scans its number plate, and generates an electronic challan. It will increase system confidence due to automatic detection that eliminates the need for costly maintenance.
Hardware and Software: For conducting the experiments, machine with NVIDIA GeForce GTX MX230 with 4 GB Ram, i5 5th generation and 500 GB HDD. For Software, we are using Tensorflow 2.0.0 framework and Python 3.7.5 with Machine Learning, YOLO and OpenCV-Python.
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