NDLI: Intelligent Highway Traffic Surveillance With Self-Diagnosis Abilities

Content Provider	IEEE Xplore Digital Library
Author	Hsu-Yung Cheng Shih-Han Hsu
Copyright Year	2000
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.
Sponsorship	IEEE Intelligent Transportation Systems Society
Starting Page	1462
Ending Page	1472
Page Count	11
File Size	816313
File Format	PDF
ISSN	15249050
Volume Number	12
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Regression analysis Surveillance Tracking Histograms Road transportation traffic parameter Headlight pairing intelligent surveillance regression analysis tracking
Content Type	Text
Resource Type	Article
Subject	Automotive Engineering Mechanical Engineering Computer Science Applications

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