To address the problem of maneuvering target tracking, where the target trajectory has prolonged smooth regions and abrupt maneuvering regions, a modified variable rate particle filter （MVRPF） is proposed. First, a Cartesian-coordinate based variable rate model is presented. Compared with conventional variable rate models, the proposed model does not need any prior knowledge of target mass or external forces. Consequently, it is more convenient in practical tracking applications. Second, a maneuvering detection strategy is adopted to adaptively adjust the parameters in MVRPF, which helps allocate more state points at high maneuver regions and fewer at smooth regions. Third, in the presence of small measurement errors, the unscented particle filter, which is embedded in MVRPF, can move more particles into regions of high likelihood and hence can improve the tracking performance. Simulation results illustrate the effectiveness of the proposed method.
针对现有基于圆柱面映射的全景图像拼接算法无法实现自动估计焦距的问题,为满足实时性要求,提出了一种基于预测的快速特征点匹配算法和基于单应矩阵的焦距修正算法。该算法首先从待拼接图像中提取Harris角点,并计算方向梯度直方图(histogram of oriented gradient,HOG)描述子,采用基于预测的快速匹配算法进行特征点匹配;然后使用简化的基于纯旋转运动的焦距估计算法估计出焦距初值后,采用基于单应矩阵的焦距修正算法得到更精确的焦距值;最后将平面图像投影至圆柱平面,使用基于加权平均融合算法进行拼接,合成全景图像。采用多个图像序列进行测试得出,特征点匹配速度较传统方法提高了10倍以上,自动焦距估计算法能够准确估计摄像机焦距。拼接实验结果表明,提出的算法能够快速地合成高质量的全景图像,拼接后的图像畸变小,具有较高的实用价值。
