Moving Vehicle Candidate Recognition and Classification Using Inception-ResNet-v2

Abstract

Vehicle detection and classification are important tasks in the automatic traffic monitoring system. The proposed work focuses on vehicle detection and classification. Vehicle detection is carried out using the combination of dense optical flow method and integrated binary projection profile. Inception-ResNet-v2 is used as a feature extraction technique and extracted features are fed to two different classifiers such as Support Vector Machine and Random Forest to classify the vehicle type. The recognition performance of Inception-ResNet-v2 with these classifiers is significantly high and the proposed approach obtained an output accuracy as 99.89% and 98.615% in Support Vector Machine and Random forest respectively.