Classification of Moving Vehicles in Traffic Videos

Abstract

AbstractIn this paper, we propose a model for classification of moving vehicles in traffic videos. We present a corner-based tracking method to track and detect moving vehicles. The detected vehicles are classified into 4 different types of vehicle classes using optimal classifiers. The proposed classification method is based on overlapping the boundary curves of each vehicle while tracking it in sequence of frames to reconstruct a complete boundary shape of it. The reconstructed boundary shape is normalized and a set of efficient shape features are extracted. Vehicles are classified by k-NN rule and the proposed weighted k-NN classifier. Experiments are conducted on 23.02 minutes of moderate traffic videos of roadway scenes taken in an uncontrolled environment during day time. The proposed method has 94.32% classification accuracy which demonstrates the effectiveness of our method. The proposed method has 87.45% of precision with 79% recall rate for classification of moving vehicles.KeywordsCorner-based trackingshape reconstructionshape normalizationshape feature extractionvehicle classificationk-nearest neighborweighted k-nearest neighbor