Vehicle detection using fuzzy C-means clustering algorithm

Vehicle detection and identification is an important function in the field of traffic control and management. Often handling this function requires working on large data sets and site-specific features. This model, which is prepared for the data, aims to recognize the elements in the image. In other words, it aims to distinguish items or assign them to the correct classes. In this study, a vehicle recognition and counting application was performed for moving images. Fuzzy C Means for image segmentation and Support Vector Machine (DVM) for image classification were preferred. It is important that these methods to be used are up to date. DVM is a current universal learning machine for both regression and pattern recognition. In machine learning, DVMs are supervised learning models that work on associated learning algorithms that analyze data and recognize patterns. Keywords: Vehicle recognition, Fuzzy C-means, Support vector machine, Clustering