Over the past decade road traffic is terribly growing all over the word. It is becoming very essential to develop automated system to automatically monitor road traffic for vehicle identification and tracking in real time environments. In this project vehicle identification and vehicle classification is achieved using computer vision and machine learning based techniques. A training/testing videos data is prepared in real-world environment. Computer vision techniques are applied for noise removal and extraction of region of interest. From the region of interest, a feature set comprised of 12 different features is extracted for car and bike classes detections to be used in ML classifiers for training the model. Machine learning algorithms are producing compelling results upto 95.83% accuracy in real world scenarios.
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