Structure and motion estimation with expectation maximization and extended Kalman smoother for continuous image sequences

This paper deals with the problem of estimating structure and motion from long continuous image sequences, applying the expectation maximization algorithm based on an extended Kalman smoother to impose time-continuity of the motion parameters. By repeatedly estimating the state transition matrix of the dynamic equation and the parameters of noise processes in dynamic and measurement equations, this optimization gives maximum likelihood estimates of the motion and structure parameters. Practically, this research is essential for dealing with a long video-rate image sequence with partially unknown system equation and noise. The algorithm is implemented and tested for a real image sequence.