应用小波模历史图像的运动车辆视频检测

为提高车辆目标检测的稳定性和准确性,提出了基于背景减除和小波分解模历史图像的运动车辆检测算法.首先对原始图像进行小波分解,对低频分量用混合高斯模型和纹理特征相结合的方法,自适应更新背景并标记运动目标初始区域;然后,基于高频分量计算模值,并通过逐帧历史累积得到模历史图像;最后,利用车辆目标与阴影相比富含边缘细节的特点,对目标进行倾斜校正后,将目标边缘分别沿图像x和y方向投影,利用投影曲线将边缘信息与目标初始区域信息迭代融合,得到最终检测结果.实验结果表明,用本文方法检测车辆的捕获率达到99.0%,有效率为92.5%;与使用单一自适应背景提取方法相比,在实际交通场景中可有效处理阴影导致的多目标粘连问题,检测结果更准确.

Vision-Based Detection of Moving Vehicles Using Wavelet Modulus History Images

To improve the robustness and accuracy of vehicle detection,a new detection method of moving vehicles was put forward based on background subtraction and wavelet decomposition modulus history images.First,wavelet decomposition was performed on the original image.In low frequency component,the Gaussian mixture model was used in conjunction with textural features to adaptively update the background image and label initial regions of moving objects.The high frequency component was used to calculate modulus value and obtain modulus history image through history frame accumulation.In view of the fact that vehicle objects have richer edge details than shadow regions,the edges were projected to x and y axes after object slant correction.Using the projection curves,the edge information was iteratively integrated with initial object regions to get final detection results.Experiment results show that compared with commonly used adaptive background extraction methods,the proposed method could detect vehicle objects accurately in practical traffic applications with a capture rate of 99.0% and an effective rate of 92.5%,and could effectively process the object conglutination caused by shadow with higher accuracy.