Moroccan Video Intelligent Transport System: Vehicle Type Classification Based on Three-Dimensional and Two-Dimensional Features

Abstract

Vehicle type classification is a critical function in any intelligent transportation system (ITS). In this paper, we present a novel two-layer vehicle type classification framework based on the vehicle's 3D parameters and its local features. This framework is a part of the first Moroccan video intelligent transport system (MOVITS) that aims to control traffic and road code violations. In the first layer, the 3D features are extracted using the disparity map generated from stereo-images, and then, the width, height, and length of the vehicle are calculated based on the obtained list of 3D points. In the second layer, a gradient-based method is applied to extract the 2D features, and a dimensional reduction algorithm is performed to reduce its size. Both features are combined to construct the final feature vector that is used as an input for the classification. The Moroccan dataset and the BIT dataset were used to, respectively, validate the proposed framework and conduct a comparative study with the state-of-the-art algorithms. The experimental results demonstrate the efficiency of our approach against existing algorithms.

Keywords

• Computer vision
• intelligent transport system
• vehicle type classification
• stereo vision
• intelligent surveillance