The main objective of this project is to identify overspeed vehicles, using Deep Learning and Machine Learning Algorithms. After acquisition of series of images from the video, trucks are detected using Haar Cascade Classifier. The model for the classifier is trained using lots of positive and negative images to make an XML file. This is followed by tracking down the vehicles and estimating their speeds with the help of their respective locations, ppm (pixels per meter) and fps (frames per second). Now, the cropped images of the identified trucks are sent for License Plate detection. The CCA (Connected Component Analysis) assists in Number Plate detection and Characters Segmentation. The SVC model is trained using characters images (20X20) and to increase the accuracy, 4 cross fold validation (Machine Learning) is also done. This model aids in recognizing the segmented characters. After recognition, the calculated speed of the trucks is fed into an excel sheet along with their license plate numbers. These trucks are also assigned some IDs to generate a systematized database.
Ikram258
commented
2 years ago
C:\Users\this pc.spyder-py3\Speed-Estimation-of-Vehicles-with-Plate-Detection-master>python Speed_Estimation_License_Plate_Detection.py Traceback (most recent call last): File "Speed_Estimation_License_Plate_Detection.py", line 884, in
trackMultipleObjects()
File "Speed_Estimation_License_Plate_Detection.py", line 467, in trackMultipleObjects
cars = carCascade.detectMultiScale(gray, 1.1, 13, 18, (24, 24))
cv2.error: OpenCV(4.2.0) C:\projects\opencv-python\opencv\modules\objdetect\src\cascadedetect.cpp:1689: error: (-215:Assertion failed) !empty() in function 'cv::CascadeClassifier::detectMultiScale'