This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods which can classify vehicles to only cars and non-cars, the proposed method has a good capability to categorize vehicles into more specific classes by introducing a new "linearity" Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make the best decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only vehicle size and single frame for classification.

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A flash flood forecasting model including a state-of-the-art data assimilation method was developed to provide a precise water stage forecast for flood emergency response. The model integrates a flash flood routing model (FFRM) coupled with an ensemble Kalman filter (EnKF) and an artificial neural network (ANN) submodel. In the model, the ANN forecasts river water stages at gauge stations first. Then, these are used as the initial and boundary conditions of the FFRM. The water stages, simulated from the FFRM, are then corrected by the EnKF for lead time. The model was applied to the Tanshui River watershed in northern Taiwan during past typhoons. The model forecasts almost covered the data observed during a typhoon period to within 95% confidence intervals. Compared with the use of FFRM without EnKF, the forecast water stages from the EnKF improved the accuracy at the conjunctions between upstream and downstream channels and the steep slope location.

This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods which can classify vehicles to only cars and non-cars, the proposed method has a good capability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by taking advantages of a line-based shadow algorithm which uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of different lane dividing lines. Therefore, an automatic scheme to detect lane dividing lines is also proposed. The found lane dividing lines also can provide important information for feature normalization, which can make the vehicle size more invariant and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make the best decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only vehicle size and single frame for classification.