| Summary |
Jie-Ru Li, 2008.06
Graduate Institute of Transportation Science, Tamkang University
Because the highly economic developing for Taiwan within the past several years, the car-hold-rate is also increasing rapidly year by year.
Therefore, the traffic problems which are from driver traffic violation are more serous than that before. Nowadays, there are many non-normal and unfair cases for the traffic management cause by the limited police resources. Although the police agency enhances to suppress illegal uses of traffic, the performance is still not good. The intelligent enforcement system platform is used to improve the traffic violation detection
performance.
In this study, the image processing techniques and the road environment recognition algorithm are used in the intelligent enforcement system platform. There are three parts: 1) the background reconstruction and its updating. 2) The road environment recognition. 3)
Traffic violation detection. Here the “Change-Lane at Will” and the “violation of Right-Lane Driving” are selected for the traffic violation detection.
In our study, the background is constructed using temporally median filter and the combining it with recursive and non-recursive background updating algorithms to update our background image. Next, the extracted features and template matching algorithm are used to obtain the lane edge trace. Hence, the road environment can be segmented via these known lane edge trace. Finally, the moving car detection and its tracking algorithms are also used to recognize the traffic violation.
The simulation results show that the temporally median filter can construct a clear background image even in the different environments.
In the road environment recognition, the detection rates of the proposed feature extraction and template matching methods are about 100% and 89.9%, respectively. In the traffic-flow counting, the detection rates of big-car, small-car and motorcycle are 84.2%, 97.1% and 100%, respectively. In the traffic violation detection, the detection rate of “Change-Lane at Will” is 100%, and the detection rate of “violation of Right-Lane Driving” is 89.1%. Althourh the detection rates were not achieve 100%, but the feasible platform was established successfully. |
