In this paper we propose a novel sparse optical flow (SOF)-based line feature tracking method for the camera pose estimation problem. This method is inspired by the point-based SOF algorithm and developed based on an observation that two adjacent images in time-varying image sequences satisfy brightness invariant. Based on this observation, we re-define the goal of line feature tracking: track two endpoints of a line feature instead of the entire line based on gray value matching instead of descriptor matching. To achieve this goal, an efficient two endpoint tracking (TET) method is presented: first, describe a given line feature with its two endpoints; next, track the two endpoints based on SOF to obtain two new tracked endpoints by minimizing a pixel-level grayscale residual function; finally, connect the two tracked endpoints to generate a new line feature. The correspondence is established between the given and the new line feature. Compared with current descriptor-based methods, our TET method needs not to compute descriptors and detect line features repeatedly. Naturally, it has an obvious advantage over computation. Experiments in several public benchmark datasets show our method yields highly competitive accuracy with an obvious advantage over speed.
翻译:在本文中,我们建议对照相机采用新的稀疏光学流(SOF)的线性跟踪方法(SOF),该方法由基于点的 SOF 算法启发,并基于对时间变化图像序列中两个相邻图像满足亮度变化变化的观察而开发。基于这一观察,我们重新确定线性跟踪目标:跟踪线性特征的两个端点,而不是基于灰色值匹配而不是基于描述符匹配的整条线。为了实现这一目标,我们提出了一个有效的两个端点跟踪方法:首先,描述以两端点为基点的给定线性特征;下一步,跟踪基于SOF的两个端点,通过尽量减少像素水平灰度残余功能获得两个新的跟踪端点;最后,将两个跟踪端点连接到产生新的线性特征。在给定的线性匹配和新线性特征之间建立了对应关系。与目前基于描述符的方法相比,我们的TET方法不需要对描述性描述和反复探测线性特征。自然,它跟踪基于SOF的两端点,其明显优势在于在公共数据基准测算中显示一个高度的精确率。