Temporal Multiple Rotation Averaging on a Distributed Dynamic Network

This article proposes a solution for multiple rotation averaging on time-series data such as video. In applications using video data such as target tracking, in addition to the data found in individual frames, temporal information across multiple frames such as target trajectories can be used to more accurately estimate target states. Existing techniques for robust rotation averaging, including traditional iterative optimization and emerging neural network methods, do not exploit this temporal information. We first introduce the problem of using temporal data in rotation averaging and propose an extension to existing multiple rotation averaging methods via temporal rrotations. We then propose implementing a motion model for the cameras and predicting camera states using a particle filter, which are used to initialize the rotation averaging algorithm. These methods' performance is evaluated through a Monte Carlo Simulation on synthetic data and compared to an existing method. The results show that using temporal data in time-series datasets significantly increases the accuracy compared to the traditional algorithm for rotation averaging.