目的 针对复杂场景图像序列中运动直线特征的提取、跟踪问题,提出一种基于点、线光流预测机制的图像序列运动直线跟踪方法。方法 首先根据图像直线的表达式定义点、线光流基本约束方程,由基本约束方程推导出关于点光流与直线光流对应关系的3个重要推论。然后依据点、线光流对应关系,利用图像序列中直线特征上的像素点光流计算直线光流的估计值并根据直线光流阈值筛选图像序列运动直线。最后由筛选出的运动直线及直线光流估计值计算直线的预测坐标并在Hough域内进行跟踪匹配,得到图像序列运动直线跟踪结果。结果 通过合成及真实图像序列实验验证,本文方法能够准确地筛选出图像序列中感兴趣的运动直线,并对运动直线进行稳定地跟踪、匹配,直线跟踪结果未产生干扰直线的误匹配,直线跟踪时间消耗不超过12 s。结论 相对于传统的直线跟踪、匹配方法,本文方法具有较高地直线跟踪精度和较好的鲁棒性,更适用于复杂场景下的运动直线跟踪、匹配问题。

Objective An image sequence moving straight-line tracking method based on the forecasting mechanism of a point and straight-line optical flow is proposed for the problem of moving straight-line feature tracking in image sequences that contain a complex scene. Method The basic constraint equation of the point and straight-line optical flow is defined; the equation is derived from the image straight-line expression. The three significant corollaries about the corresponding relationship between the point and straight-line optical flow are deduced based on the basic constraint equation of the point and straight-line optical flow. Then, with the corresponding relationship between the point and straight-line optical flow, straight-line optical flow is estimated with the defined computing model through the mean filtering strategy. With the estimated straight-line optical flow, the moving straight line can be extracted by the given straight-line optical flow threshold. Finally, with the extracted moving straight line and the estimated straight-line optical flow, the prediction coordinate of the moving straight-line in the next frame is obtained. The moving straight-line tracking result is acquired by comparing and selecting the minimum Euclidean distances between the moving straight-line prediction coordinate and the actual coordinates of the next frame in the neighborhood window of the Hough space. Result To demonstrate the performance of the proposed method in moving straight-line tracking, three elaborate synthetic and real image sequence experiments are conducted. In the experiments, the effects of optical flow estimation, straight-line optical flow threshold, and size of the straight-line matching window are demonstrated. Experimental results show thatthe proposed method can accurately extract and track the moving straight-line; it can also track and match the moving straightline stably. No wrong matching is noted for the interference straight lines of the straight-line tracking result, and the time consumption of the straight-line tracking method is not more than 12 s. Conclusion Compared with traditional moving straight-line tracking and matching methods, the proposed method has higher straight-line tracking accuracy and better robustness; this condition indicates that the proposed method is more applicable to the problem of moving straight-line tracking in complex scenes.