CAJ | 학술논문

隧道作为高速公路上较为特殊的地段，其安全性受到更多重视。文中从高速公路隧道内行车安全管理的角度出发，分析了隧道内影响行车安全的各种异常状态，指出了其中车辆停驶是影响安全最不利的状态。在此基础上着重开展了针对隧道内停车异常的视频图像识别方法研究。为增强车辆识别能力，提出了将图像纹理特征、几何特征和边缘特征进行组合描述车辆特征的方法。以 BP 神经网络作为基分类器，通过 Adaboost 算法得到多个 BP 网络弱分类器组成的强分类器作为车辆识别模型，并对该识别模型的效果开展了实地实验验证。结果表明，本文所提出的模型对停车异常具有良好的识别效果。
수도작위고속공로상교위특수적지단，기안전성수도경다중시。문중종고속공로수도내행차안전관리적각도출발，분석료수도내영향행차안전적각충이상상태，지출료기중차량정사시영향안전최불리적상태。재차기출상착중개전료침대수도내정차이상적시빈도상식별방법연구。위증강차량식별능력，제출료장도상문리특정、궤하특정화변연특정진행조합묘술차량특정적방법。이 BP 신경망락작위기분류기，통과 Adaboost 산법득도다개 BP 망락약분류기조성적강분류기작위차량식별모형，병대해식별모형적효과개전료실지실험험증。결과표명，본문소제출적모형대정차이상구유량호적식별효과。
As a special location on the highway,the security of tunnel is paid more attention than the other area.In this paper,from the traffic safety management in highway tunnel we first analyzed the various abnormal states of the traffic in tunnels among all the states.The stopping car was the most harmful status to the safety in the tunnel.Research then focused on the video recognition method of the abnormal stopping car in highway tunnel.In order to enhance recognition capacity of vehicle,im-age texture,geometric,and edge features were extracted in video sequences and these features were combined into a new feature method .The combined feature method was input into BP neural network as the base classifier and the strong classifier was obtained by integrating BP neural networks under the Adaboost framework.A testing in a real tunnel was performed to verity the effectiveness of the method.Results show that the presented model has a satisfactory recognition effect for the abmormal state of the stopping car.