Abstract: In complex outdoor environment, there are many limitations when using imaging processing algorithm. This paper proposes an adaptive vehicle extraction algorithm for real-time traffic surveillance system. In order to enhance the ability of adaptation to illumination changes, shadows and system noise in long-term running, the algorithm uses both morphological operation and edge histogram method to extract foreground vehicle objects. And it maintains certain accuracy and processing speed. Experiment on real-world video shows a rather satisfying result. Thus, this offers good foreground information for further vehicle tracking.

Key words: morphological operation, edge histogram, vehicle extraction

摘要： 在复杂的室外环境中，视频处理技术的应用受到了一定的限制。该文提出一种实时交通视频下自适应的车辆提取算法，提高了视频监测系统在长时间运行条件下对光线变化、阴影和系统噪声的适应性。算法利用图像形态学和图像边缘直方图运算的2种方法对前景目标车辆进行提取，保证了算法的准确率和效率。实时交通视频实验结果表明，该方法取得了较好的成果，为后续的运动车辆跟踪提供了前景信息。

关键词: 形态学运算, 边缘直方图, 车辆提取

CLC Number: 

• TN911.73