DocumentCode :
1327996
Title :
Intelligent Highway Traffic Surveillance With Self-Diagnosis Abilities
Author :
Cheng, Hsu-Yung ; Hsu, Shih-Han
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chungli, Taiwan
Volume :
12
Issue :
4
fYear :
2011
Firstpage :
1462
Lastpage :
1472
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.
Keywords :
Kalman filters; image sequences; object detection; regression analysis; traffic engineering computing; video surveillance; Kalman filter; intelligent highway traffic surveillance; regression analysis; self-diagnosis abilities; state transition matrix; time-varying adaptive system; vehicle detection; Histograms; Regression analysis; Road transportation; Surveillance; Tracking; Headlight pairing; intelligent surveillance; regression analysis; tracking; traffic parameter;
fLanguage :
English
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
1524-9050
Type :
jour
DOI :
10.1109/TITS.2011.2160171
Filename :
6026251
Link To Document :
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