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Research on Vehicle Detection Method Based on Video Image

机译:基于视频图像的车辆检测方法研究

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摘要

Nowadays, there are quite a few methods that have their respective advantages and disadvantages to update the background, such as method of multiframes average, method of Gaussian distribution models etc.. Considering those disadvantages above, the background updating algorithm which be advanced in the research can reduce the effect by shadow effectively by doing the calculation of H chrominance component on HSI space, achieving a lasting update automatically by operating the frequency statistics after every sampling. Applying the method of combining edge detection of morphologic and difference of background together to the target detection, a method of foreground area extraction based on the edge information has been brought forward in this research. The method of morphologic edge detection algorithm has a good restrain on noises while doing the edge detection. Therefore, in vehicle detection, the background difference will be done after respective morphologic edge detection of the current video image and background image. Use the extraction template of background edge to extract the precise background edge of the current frame, then a foreground edge of the vehicle will be extracted, finally use the mathematical morphology to do the later treatment to the result of target dividing, remove the noise. The experiment has proved that the measure mentioned above has increased the accuracy and stability of vehicle detection effectively.
机译:如今,背景更新的方法有很多,各有其优缺点,例如多帧平均法,高斯分布模型法等。考虑到上述缺点,本研究提出了背景更新算法。通过对HSI空间上的H色度分量进行计算,可以有效地减少阴影效应,并在每次采样后通过操作频率统计信息自动实现持久更新。将形态学边缘检测和背景差异相结合的方法结合到目标检测中,提出了一种基于边缘信息的前景区域提取方法。形态学边缘检测算法在进行边缘检测时对噪声有很好的抑制作用。因此,在车辆检测中,背景差异将在当前视频图像和背景图像各自的形态学边缘检测之后进行。使用背景边缘提取模板提取当前帧的精确背景边缘,然后提取车辆的前景边缘,最后使用数学形态学对目标分割结果进行后续处理,去除噪声。实验证明,上述措施有效提高了车辆检测的准确性和稳定性。

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