21 June 2016 Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera
Author Affiliations +
Abstract
Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade–Lucas–Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.
© 2016 SPIE and IS&T
Redouane Kachach, Redouane Kachach, José María Cañas, José María Cañas, } "Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera," Journal of Electronic Imaging 25(3), 033021 (21 June 2016). https://doi.org/10.1117/1.JEI.25.3.033021 . Submission:
JOURNAL ARTICLE
24 PAGES


SHARE
Back to Top