基于运动目标三维轨迹重建的视频序列同步算法

提出一种利用运动目标三维轨迹重建的视频时域同步算法.待同步的视频序列由不同相机在同一场景中同时拍摄得到，对场景及相机运动不做限制性约束.假设每帧图像的相机投影矩阵已知，首先基于离散余弦变换基重建运动目标的三维轨迹.然后提出一种基于轨迹基系数矩阵的秩约束，用于衡量不同序列子段间的空间时间对准程度.最后构建代价矩阵，并利用基于图的方法实现视频间的非线性时域同步.我们不依赖已知的点对应关系，不同视频中的跟踪点甚至可以对应不同的三维点，只要它们之间满足以下假设：观测序列中跟踪点对应的三维点，其空间位置可以用参考序列中所有跟踪点对应的三维点集的子集的线性组合描述，且该线性关系维持不变.与多数现有方法要求特征点跟踪持续整个图像序列不同，本文方法可以利用长短不一的图像点轨迹.本文在仿真数据和真实数据集上验证了提出方法的鲁棒性和性能.

Synchronization of Video Sequences Through 3D Trajectory Reconstruction

We present an algorithm for synchronization of an arbitrary number of videos captured by cameras independently moving in a dynamic 3D scene. Assuming the 3D spatial poses of the cameras are known for each frame, we first reconstruct the 3D trajectory of a moving point using the trajectory basis-based method. The trajectory coefficients are computed for each sequence separately. Point correspondences across sequences are not required, or even it is possible to track different points in different sequences, only if every 3D point tracked in the second sequence is a linear combination of subsets of the 3D points tracked in the first sequence. Then we propose use a robust rank constraint of the coefficient matrices to measure the spatio-temporal alignment quality for every feasible pair of video fragments. Finally, the optimal temporal mapping is found using a graph-based approach. Our algorithm can use both short and long feature trajectories, and it is robust to mild outliers. We verify the robustness and performance of the proposed approach on synthetic data as well as on challenging real video sequences.