Intelligent highway traffic surveillance with self-diagnosis abilities

Hsu Yung Cheng, Shih Han Hsu

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


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.

Original languageEnglish
Article number6026251
Pages (from-to)1462-1472
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number4
StatePublished - Dec 2011


  • Headlight pairing
  • intelligent surveillance
  • regression analysis
  • tracking
  • traffic parameter


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