Real-time Detection and Tracking Method of Landmark Based on UAV Visual Navigation

无人机视觉导航着陆地标实时检测跟踪方法

¹ School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
² School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
³ National Key Laboratory of Special Technology on UAV, Northwestern Polytechnical University, Xi'an 710072, China

Received: 2 June 2017

Abstract

A real-time detection and tracking method of landmark based for UAV visual navigation and fixed landing was proposed. This method used the SVM classification algorithm to train the offline classifier based on SURF-BoW features extracted from samples, which can recognize the landing landmark accurately and complete the initialization of the tracker. Afterwards, tracked the landmark via the improved median flow algorithm to ensure the reliability and integrity of the tracking target. Finally, based on the classifier and the principle of similarity between two adjacent frames' target, this paper designed a target re-search algorithm to ensure that the target can be retrieved quickly even if the target is lost or the target tracking fails, which makes the entire set of algorithm track the target accurately for a long time. The experimental results show that the proposed algorithm has good tracking performance under the conditions of the change of target scale, illumination changes and motion blur.

摘要

以改进的中值流跟踪算法为核心，基于上下文信息相关性特点，提出了一种适用于无人机视觉导航定点着陆的着陆地标实时检测跟踪方法。以SURF-BoW特征来描述样本图像，基于支持向量机（SVM）进行离线分类器训练，用于准确的识别目标，完成跟踪目标初始化。之后，利用改进后的中值流跟踪算法进行目标跟踪，保证目标跟踪的可靠性、完整性。最后，基于相邻2帧目标相似性原则与线下训练的分类器，设计目标再搜索算法，保证在目标丢失或目标跟踪失败的情况下，仍然能够快速地找回目标，使得整套算法能够长时间准确地跟踪目标。实验结果表明，该算法在目标尺度变化、光照变化及运动模糊等情况下，都能够实时、稳定地跟踪目标。