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首页> 外文期刊>Journal of visual communication & image representation >Effective vehicle detection technique for traffic surveillance systems
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Effective vehicle detection technique for traffic surveillance systems

机译：用于交通监控系统的有效车辆检测技术

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摘要

Moving object detection is one of the key technologies for intelligent video monitoring systems. For real-time detection of moving object in the surveillance scene, the general and simple method is based on background image difference. However, it requires the accurate current background image and the approach for automatic background updating along with the illumination variance is difficult to design and implement. This limits its applications. To solve the above problem, a new self-adaptive background approximating and updating algorithm based on optical flow theory is presented for the traffic surveillance scene in this paper. To detect the moving regions of interest in the scene, the difference image between the current frame and the updating background is first obtained by using a color image difference model, and then a self-adaptive thresholding segmentation method for moving object detection based on the Gaussian model is developed and implemented. Moreover, an effective shadow-eliminating algorithm based on contour information and color features is developed. Experimental results demonstrate that the proposed background updating method can update the background exactly and rapidly along with the variance of illumination, the self-adaptive thresholding segmentation method based on the Gaussian model can extract the moving object regions accurately and completely, and the shadow can be eliminated accurately. This is the foundation for further objects recognition and understanding.
机译：运动目标检测是智能视频监控系统的关键技术之一。对于监视场景中运动物体的实时检测，一般和简单的方法是基于背景图像差异。然而，这需要准确的当前背景图像，并且难以设计和实现用于自动背景更新以及照明变化的方法。这限制了它的应用。针对上述问题，提出了一种基于光流理论的交通监控现场自适应背景逼近与更新算法。为了检测场景中感兴趣的运动区域，首先使用彩色图像差异模型获取当前帧与更新背景之间的差异图像，然后基于高斯的自适应阈值分割方法进行运动物体检测。模型的开发和实施。此外，开发了一种基于轮廓信息和颜色特征的有效阴影消除算法。实验结果表明，所提出的背景更新方法可以随着光照的变化准确，快速地更新背景，基于高斯模型的自适应阈值分割方法可以准确，完整地提取运动物体区域，并且阴影可以被提取。准确消除。这是进一步识别和理解对象的基础。

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