Improving Vehicle Detection Accuracy Based on Vehicle Shadow and Superposition Elimination
Hongjin Zhu*, 1, Honghui Fan1, Feiyue Ye1, 2, Xiaorong Zhao1
Identifiers and Pagination:Year: 2015
First Page: 1039
Last Page: 1044
Publisher Id: TOMEJ-9-1039
Article History:Received Date: 17/02/2014
Revision Received Date: 21/03/2015
Acceptance Date: 09/06/2015
Electronic publication date: 7/10/2015
Collection year: 2015
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Vehicle shadow and superposition have a great influence on the accuracy of vehicles detection in traffic video. Many background models have been proposed and improved to deal with detection moving object. This paper presented a method which improves Gaussian mixture model to get adaptive background. The HSV color space was used to eliminate vehicle shadow, it was based on a computational colour space that makes use of our shadow model. Vehicle superposition elimination was discussed based on vehicle contour dilation and erosion method. Experiments were performed to verify that the proposed technique is effective for vehicle detection based traffic surveillance systems. Detection results showed that our approach was robust to widely different background and illuminations.