在结构化场景的轨道交通中,车载视频观测因相机平移运动而呈现出图像内容以某点为中心向四周扩散的现象,该点被称为FOE(Focus of Expansion)。当前计算FOE的算法对噪声敏感且计算量大,不能准确地计算铁路场景中的FOE。鉴于此,文中提出...在结构化场景的轨道交通中,车载视频观测因相机平移运动而呈现出图像内容以某点为中心向四周扩散的现象,该点被称为FOE(Focus of Expansion)。当前计算FOE的算法对噪声敏感且计算量大,不能准确地计算铁路场景中的FOE。鉴于此,文中提出一种铁路视频序列的FOE估计方法。该方法首先利用金字塔光流法对检测的Harris角点进行跟踪和粗匹配,并在此基础上利用RANSAC算法进行精确的匹配,求得基础矩阵,然后提取图像中的极线束并计算FOE。实验结果表明,所提算法比Hough直线求得的FOE误差小,适于实时应用。展开更多
Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying proces...Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying process,tracking and recognition method combined with an affine transformation was proposed.The method can be divided into three steps.First,the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue(2R-G-B)color feature;after improving the binary image,the apples were recognized with a local parameter adaptive Hough circle transformation method,thus improving the accuracy of recognition and avoiding the long,time-consuming process and excessive fitted circles in traditional Hough circle transformation.The process and results were verified experimentally.Second,the Shi-Tomasi corners detected and extracted from the first frame image were tracked,and the corners with large positive and negative optical flow errors were removed.The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm(RANSAC)to correct the scale of the template image and predict the apple positions.Third,the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm.The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8%and 20%in the absence of template correction and apple position prediction,respectively.In comparison,the running time of our algorithm was 25 ms,and the tracking error was less than 4%.Therefore,test results indicate that speed and efficiency can be greatly improved by using our method,and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits.展开更多
文摘在结构化场景的轨道交通中,车载视频观测因相机平移运动而呈现出图像内容以某点为中心向四周扩散的现象,该点被称为FOE(Focus of Expansion)。当前计算FOE的算法对噪声敏感且计算量大,不能准确地计算铁路场景中的FOE。鉴于此,文中提出一种铁路视频序列的FOE估计方法。该方法首先利用金字塔光流法对检测的Harris角点进行跟踪和粗匹配,并在此基础上利用RANSAC算法进行精确的匹配,求得基础矩阵,然后提取图像中的极线束并计算FOE。实验结果表明,所提算法比Hough直线求得的FOE误差小,适于实时应用。
基金This work was financially supported by Basic Public Welfare Research Project of Zhejiang Province(Grant No.LGN20E050007).
文摘Apple fruits on trees tend to swing because of wind or other natural causes,therefore reducing the accuracy of apple picking by robots.To increase the accuracy and to speed up the apple tracking and identifying process,tracking and recognition method combined with an affine transformation was proposed.The method can be divided into three steps.First,the initial image was segmented by Otsu’s thresholding method based on the two times Red minus Green minus Blue(2R-G-B)color feature;after improving the binary image,the apples were recognized with a local parameter adaptive Hough circle transformation method,thus improving the accuracy of recognition and avoiding the long,time-consuming process and excessive fitted circles in traditional Hough circle transformation.The process and results were verified experimentally.Second,the Shi-Tomasi corners detected and extracted from the first frame image were tracked,and the corners with large positive and negative optical flow errors were removed.The affine transformation matrix between the two frames was calculated based on the Random Sampling Consistency algorithm(RANSAC)to correct the scale of the template image and predict the apple positions.Third,the best positions of the target apples within 1.2 times of the prediction area were searched with a de-mean normalized cross-correlation template matching algorithm.The test results showed that the running time of each frame was 25 ms and 130 ms and the tracking error was more than 8%and 20%in the absence of template correction and apple position prediction,respectively.In comparison,the running time of our algorithm was 25 ms,and the tracking error was less than 4%.Therefore,test results indicate that speed and efficiency can be greatly improved by using our method,and this strategy can also provide a reference for tracking and recognizing other oscillatory fruits.
