Machine Learning based Vehicle Counting and Detection System


  • Jayamalar T Avinashilingam Institute for Home Science and Higher Education for Women
  • N. Krishnaveni Avinashilingam Institute for Home Science and Higher Education for Women



Vehicle Detection, YOLOv3, SVM, Histogram, Sliding Window


The study of how machines perceive instead of humans is known as vehicle detection or computer vision object identification. The primary purpose of a vehicle detection system is to identify one or multiple vehicles within the input images and live video feed. The dataset is used to train image processing algorithms for tasks like detection and tracking. To pinpoint the defects and strength of each image processing system, assessment criteria are used to develop, train, test, and compare them. To recognize, track, and count the vehicle in images and videos, the image processing algorithms such as CNN YOLOv3 and SVM are implemented. The main goal and intention of this work is to develop a system that can intelligently identify and track automobiles in still images and moving movies. The results demonstrated that CNN-based YOLOv3 does a  good job of detecting and tracking vehicles.




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