基于TLD算法的加油锥套跟踪

针对自主空中加油对接阶段锥套跟踪问题，提出了一种基于tracking learning detection (TLD)的锥套跟踪算法。该算法将加油锥套的跟踪任务分解成跟踪、学习、检测3个部分。跟踪模块在LK光流法的基础上添加跟踪失败自检测，筛选出好的跟踪点，跟踪加油锥套；检测模块构建级联分类器，对滑动窗遍历得到的图像块进行分类并返回含有目标的图像块，融合跟踪模块的跟踪框，给出最终跟踪结果；学习模块引入P N约束修正错误样本并学习更新检测模块。利用Creator/Vega Prime软件对空中加油进行视景仿真，在视景仿真视频上测试锥套跟踪算法。结果表明:TLD算法跟踪加油锥套成功率达95.5%，处理每帧平均耗时31.4 ms，能够满足加油锥套跟踪鲁棒性、准确率、实时性的要求。

Refueling drogue tracking based on tracking learning detection algorithm

To solve the drogue tracking problem during the capture phase of autonomous aerial refueling, a tracking learning detection (TLD) algorithm was proposed. The algorithm decomposes the drogue tracking task into 3 sub tasks: tracking, learning and detection. The tracking component is based on the Lucas Kanade (LK) method extended with failure detection and chooses tracking points with good performance to track the refueling drogue; and the detector constructs cascaded classifier to sort the picture patches scanned by scanning window and returns patches containing object, which integrates with tracking component result and gives final tracking result; and then the P N constraints are introduced into the learning component to correct wrong samples and then learn and update the detector. A scene simulation for aerial refueling was developed based on Creator/Vega Prime. The experimental results of the test on the video of scene simulation show that the success rate of tracking drogue is 95.5%, the average time consumption is about 31.4 ms, and the algorithm can meet well requirements of drogue tracking, such as robustness, precision and real time performance.