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Merging artificial objects with marker-less video sequences based on the interacting multiple model method

机译:基于交互多模型方法的无标记视频序列与人工对象的合并

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Inserting synthetic objects into video sequences has gained much interest in recent years. Fast and robust vision-based algorithms are necessary to make such an application possible. Traditional pose tracking schemes using recursive structure from motion techniques adopt one Kalman filter and thus only favor a certain type of camera motion. We propose a robust simultaneous pose tracking and structure recovery algorithm using the interacting multiple model (IMM) to improve performance. In particular, a set of three extended Kalman filters (EKFs), each describing a frequently occurring camera motion in real situations (general, pure translation, pure rotation), is applied within the IMM framework to track the pose of a scene. Another set of EKFs,one filter for each model point, is used to refine the positions of the model features in the 3-D space. The filters for pose tracking and structure refinement are executed in an interleaved manner. The results are used for inserting virtual objects into the original video footage. The performance of the algorithm is demonstrated with both synthetic and real data. Comparisons with different approaches have been performed and show that our method is more efficient and accurate.
机译:近年来,将合成对象插入视频序列引起了人们的极大兴趣。快速可靠的基于视觉的算法对于使这种应用成为可能是必要的。使用来自运动技术的递归结构的传统姿态跟踪方案采用一个卡尔曼滤波器,因此仅支持某种类型的摄像机运动。我们提出了一种健壮的同时姿势跟踪和结构恢复算法,该算法使用交互多模型(IMM)来提高性能。特别是,在IMM框架内应用了一组三个扩展的卡尔曼滤波器(EKF),每个滤波器描述真实情况下(通常,纯平移,纯旋转)经常发生的摄像机运动,以跟踪场景的姿势。另一套EKF(每个模型点一个滤波器)用于完善3D空间中模型特征的位置。用于姿态跟踪和结构优化的过滤器以交错方式执行。结果用于将虚拟对象插入原始视频素材中。综合和真实数据都证明了该算法的性能。进行了不同方法的比较,结果表明我们的方法更加有效和准确。

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