Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TITS.2011.2160171 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
intelligent highway traffic surveillance,kalman filter,kalman filters,vehicle detection,better prediction,traffic engineering computing,regression analysis,traffic parameter,time-varying adaptive system state,traffic surveillance scene,time-varying adaptive system,headlight pairing,traffic flow parameter,self-diagnosis abilities,tracking,image sequences,proposed system,object detection,self-diagnosing intelligent highway surveillance,intelligent surveillance,state transition matrix,nighttime traffic surveillance,different traffic condition,daytime surveillance,video surveillance,lighting,machine vision,histograms,state transition,adaptive system,traffic flow,algorithms | Object detection,Computer vision,Self-diagnosis,Traffic flow,Machine vision,Simulation,Adaptive system,Floating car data,Robustness (computer science),Kalman filter,Artificial intelligence,Engineering | Journal |
Volume | Issue | ISSN |
12 | 4 | 1524-9050 |
Citations | PageRank | References |
19 | 1.07 | 23 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsu-Yung Cheng | 1 | 243 | 23.56 |
| Shih-Han Hsu | 2 | 21 | 1.44 |