Accident Detection and Number Plate Recognition using Image Processing and Machine Learning
Abhishek L1, Aravindhan S2, T Raghav Kumar3
1Abhishek L, School of Computing Science and Engineering, Vellore Institute of Technology Chennai, Chennai (Tamil Nadu), India.
2Aravindhan S, School of Computing Science and Engineering, Vellore Institute of Technology Chennai, Chennai (Tamil Nadu), India.
3T Raghav Kumar, School of Computing Science and Engineering, Vellore Institute of Technology Chennai, Chennai (Tamil Nadu), India.
Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 5079-5083 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6964018520/2020©BEIESP | DOI: 10.35940/ijrte.E6964.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: -The purpose of this project is to detect the accident before it happens along with theextraction the number plate. Different image processing techniques along with morphological operators and Canny Edge Detection are used for image enhancements and object outline detections. With analysis of continuous frames, the relative velocity and the distance from which the leading vehicles are moving could be computed which is further helpful in accident detection and thus prevention too. Histogram of Oriented Gradients (HOG features) are used for feature extraction. Different machine learning classification algorithms like SVM, MLP, and XGBoost are used for classification of the object. Different standard OCR tools like Pytesseract, PyOCR, TesserOCR are used for the retrieval of the vehicle number from the extracted licence plate sub-image.
Keywords: Accident Detection, Vehicle Collision Avoidance, Licence Plate Recognition, Image Processing, Morphological Operators, Canny Edge Detection, Histogram of Oriented Gradients, HOG, Machine Learning, XGBoost, Multilayer Perceptron, MLP, Support Vector Machine, SVM.
Scope of the Article: Machine Learning.