| Summary |
Chen Chang-Chih，2007.06
Graduate Institute of Transportation Science, Tamkang University
A good traffic surveillance system must be capable of working in all kinds of weather and illumination conditions. Using image detection machine usually does not effective because it be affected by weather, bright, and complex traffic background. If we can choose good detection algorithms for vehicle detection depend on weather and illumination change. This paper presents reconstruction of background model and classification model to solve the problem of detect algorithms transform.
The result of classified traffic background depends on interaction of illumination change and color model. We can judge what kinds of weather and illumination by using our eyes and ears to choose suitable image detection algorithms. But if machine want to choose fit detection algorithms, it have to depend on capture frame. How to judge conditions of illumination and give a suitable suggestion for detection is this paper kernel. First, this paper presents a recursive median filter background model to remove vehicles form frame. It can avoid interference form vehicles and illumination change, which affect detective area. Second, according to color distribution, which retrieves from detection frame, the paper presents fuzzy-neuron network to classify all kinds of weather and illumination conditions. The result displays that color have similar distribution at the similar traffic condition, and it will contribute to classification of traffic background pattern model.
The experimental place is in outdoors. The weather includes sunny day and rainy day, and the illumination change includes afternoon, nightfall, and night. According to our experimental analysis, the accurate rate of weather classified is 98.11 %,and illumination classified is 94.34 %.Then the accurate rate of weather and illumination is 92.45 %.
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