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Real-time UAV Target Tracking System Based on Optical Flow and Particle Filter Integration

机译：基于光流量和粒子滤波器集成的实时UAV目标跟踪系统

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摘要

This paper presents a design and implementation of a real-time, vision-based target tracking system for unmanned aerial vehicle (UAV). The particle filter framework integrated with Lucas-Kanade optical flow technique to predict and correct the state of the moving target based on its dynamic and observation models. The optical flow estimates the corresponding feature points in the new image frame related to the previously detected/estimated points. The Maximum Likelihood Estimation SAmple Consensus (MLESAC) method is applied to estimate the ego-motion transformation matrix using the old and new sets of the feature points. This matrix is incorporated with the target dynamic model to give more accurate prediction results of its state. Two optimized types of features are extracted to build the target observation model. They include extended Haar-like rectangles and edge orientation histogram (EOH) features. A Gentle AdaBoost classifier is applied on these features to distinguish and choose the best predefined number of features that highly represent the target. The vectorization approach is used to reduce the calculation cost due to the matrix manipulations. The proposed tracking system is tested on different scenarios of the on-time modified VIVID database and achieved real time tracking speed with 95% successful tracking rate.
机译：本文介绍了无人驾驶飞行器（UAV）的实时视觉的目标跟踪系统的设计和实现。基于其动态和观测模型，与Lucas-Kanade光流技术集成的粒子过滤器框架与Lucas-Kanade光学流技术相结合，并校正了移动目标的状态。光流程估计与先前检测到/估计点相关的新图像帧中的相应特征点。应用最大似然估计样本共识（MLESAC）方法使用特征点的旧和新组来估计自我运动转换矩阵。该矩阵与目标动态模型结合，以提供其状态的更准确的预测结果。提取两个优化类型的特征以构建目标观察模型。它们包括扩展哈尔样矩形和边缘方向直方图（EOH）功能。应用温和的AdaBoost分类器应用于这些功能，以区分和选择高度代表目标的最佳预定义数量。矢量化方法用于降低由于矩阵操纵引起的计算成本。所提出的跟踪系统在适时修改的生动数据库的不同场景上进行测试，并实现了95％成功跟踪速率的实时跟踪速度。

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