In view of the problems in the real-time traffic video monitoring that the adaptive vehicle extraction is greatly affected by the environmental factors such as the illumination, noise, and so on; the missed detection and error detection rate is high; and it is difficult to meet the robustness and the real-time performance at the same time, a kind of method for the adaptive vehicle extraction in real-time traffic video monitoring based on the fusion of multi-objective particle swarm optimization algorithm is put forward. In this method, based on the multi-objective particle swarm optimization algorithm, adaptive binarization processing is carried out on the image first, and the interference points are removed by filtration through the erosion and expansion method. Simple and effective method is used to carry out the merger of the shadow line and the extraction of the real-time traffic video. In the algorithm, the information entropy in the target area and the symmetry characteristics of the vehicle tail are used to screen and identify the region of interest, which has reduced the missed detection and error detection rate of the algorithm. The multi-objective particle swarm optimization algorithm is used to extract the vehicle boundaries and has achieved relatively good effect. The results show that the detection accuracy is 89% and the average operating speed is 17.6 frames/s during the processing of the real-time traffic video with the resolution of 640 × 480.

中文翻译：

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实时交通视频自适应车辆提取监测基于多目标粒子群算法的融合。

鉴于在实时交通视频监控，自适应车辆提取大大受环境因素如照明的问题，噪声，等等; 漏检和错误检测率较高; 它是难以满足的鲁棒性，并在同一时间的实时性能，是一种用于实时交通视频自适应车辆提取方法监测基于多目标粒子群算法的融合，提出了。在该方法中，基于多目标粒子群优化算法，自适应二值化处理是在图像上进行的第一个，通过侵蚀和膨胀法过滤除去干扰点。简单有效的方法来进行影线的合并和实时交通视频的提取。在该算法中，在目标区域中的信息熵与车辆尾部的对称特性被用于筛选和鉴定的感兴趣的区域，这降低了算法的漏检和误检测率。多目标粒子群优化算法被用于提取车辆的边界，并已取得比较好的效果。结果表明，检测精度为89％，平均运行速度是17.6帧/秒与640×480分辨率的实时交通视频的处理过程中。在目标区域和车辆尾部的对称特性的信息熵被用于筛选和鉴定的感兴趣的区域，这降低了算法的漏检和误检测率。多目标粒子群优化算法被用于提取车辆的边界，并已取得比较好的效果。结果表明，在分辨率为640×480的实时交通视频处理过程中，检测精度为89％，平均工作速度为17.6帧/秒。在目标区域和车辆尾部的对称特性的信息熵被用于筛选和鉴定的感兴趣的区域，这降低了算法的漏检和误检测率。多目标粒子群优化算法被用于提取车辆的边界，并已取得比较好的效果。结果表明，检测精度为89％，平均运行速度是17.6帧/秒与640×480分辨率的实时交通视频的处理过程中。