Research On Key Technologies Of Video-based Parking Detection In Complex Scenes

Illegal parking is an important class of abnormal traffic incidents in highway videomonitoring system. Once the illegal parking event happens, it will easily lead to trafficjams and even secondary accident without timely processing due to the heavy trafficand high-speed cars in the motorway. And that would seriously affect the normaloperation of the speedway. Furthermore, there is more interferential information in thevideo screen of complex scenes, which is possible to decrease the accuracy rate ofdetection, and even to be completely ineffective. The existing illegal parking detectionalgorithms based on monitoring system are just for some unique scenes and they cannotbe well adapted to the environment. In fact, the actual application scenarios are muchmore complex, including bad weathers, interference of car lights and other light sources,camera shakes, barriers between the vehicles and so on. Therefore, to research the roadparking event detection in complex scenes has important theoretical and practicalsignificance.To solve these problems, combined with the characteristics of the typical tunneland open road scenes, this paper focuses on two aspects, which are moving targetdetection and stopped vehicle identification, to research the key technologies of thedetection, background modeling, background updating, stopped vehicle identificationand so on.On the aspect of background modeling and updating, this thesis brings up onemethod of statistical neighborhood maximum value, which combines with the methodof statistical histogram and the method of statistical median. The method not only canfilter out the impact of the vehicle lighting on the background, but also can establish areliable background through obtaining the median pixel, what’s more, it can improveefficiency more effectively by this method. In addition, it is necessary to update thebackground in real time since the complex environment always change in reality. Someexisting background update algorithms even make the vehicles which have stopped forlong time updated to the background. To solve these problems, this thesis will utilize abackground update method based on block images, which is also need themorphological filtering as an auxiliary.On the side of identifying stopped vehicles, to reduce the effect of the moving foreground to the pixels of background, this thesis comes up with a method to analyzethe changes of background pixels which is one steady-state analysis based on timeseries features of pixels. Furthermore, it can detect the stationary targets much moreeffectively. On this basis, use the special characteristics of vehicles, like the contourfeatures and duty ratio to identification vehicles, which can tell different spilled materialapart.In the thesis, two typical traffic scenes, tunnel and open road scenes of highway areused to verify the parking detection algorithm. The results demonstrate that the positivedetection rate in the tunnel scene can reach more than95%, which also can do morethan90%in the open road scene of the motorway. Compared with the other algorithms,it not only reduces computations and improves the system real-time, but also with betteraccurate detection rate.