Abstract
In this paper, the authors propose a model for classification of moving vehicles in traffic videos. A corner-based tracking method is presented to track and detect moving vehicles. The authors propose to overlap the boundary curves of each of the detected moving vehicles while tracking in a sequence of frames to reconstruct a complete boundary shape of the vehicle. The reconstructed boundary shape is normalized and then shape features are extracted. Vehicles are categorized into 4 different types of vehicle classes using KNN rule, the weighted KNN, PNN, and SVM classifiers. Experiments are conducted on traffic video sequences captured in an uncontrolled environment during daytime.
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