Intelligent Highway Traffic Surveillance With Self-Diagnosis Abilities

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/51634

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51634

Title:	Intelligent Highway Traffic Surveillance With Self-Diagnosis Abilities
Authors:	Cheng,HY;Hsu,SH
Contributors:	資訊工程學系
Keywords:	LANE DETECTION;TRACKING;VEHICLE;EXTRACTION
Date:	2011
Issue Date:	2012-03-27 18:58:04 (UTC+8)
Publisher:	國立中央大學
Abstract:	In this paper, we propose a self-diagnosing intelligent highway surveillance system and design effective solutions for both daytime and nighttime traffic surveillance. For daytime surveillance, vehicles are detected via background modeling. For nighttime videos, headlights of vehicles need to be located and paired for vehicle detection. An algorithm based on likelihood computation is developed to pair the headlights of vehicles at night. Moreover, to balance between the robustness and abundance of acquired information, the proposed system adapts different strategies under different traffic conditions. Performing tracking would be preferred when traffic is smooth. However, under congestion conditions, it is better to obtain traffic parameters by estimation. We utilize a time-varying adaptive system state transition matrix in Kalman filter for better prediction in a traffic surveillance scene when performing tracking. We also propose a mechanism for estimating the traffic flow parameter via regression analysis. The experimental results have shown that the self-diagnosis ability and the modules designed for the system make the proposed system robust and reliable.
Relation:	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	421	View/Open

社群 sharing

Loading...