Stochastic filtering for motion trajectory in image sequences using a Monte Carlo filter with estimation of hyper-parameters

False matching due to errors in feature extraction and changes in illumination between frames may occur in feature tracking in image sequences. False matching leads to outliers in feature motion trajectory. One way of reducing the effect of outliers is stochastic filtering using a state space model for motion trajectory. Hyper-parameters in the state space model, e.g., variances of noise distributions, must be determined appropriately to control tracking motion and outlier rejection properly. Likelihood can be used to estimate hyper-parameters, but it is difficult to apply online tracking due to computational cost. To estimate hyper-parameters online, we include hyper-parameters in state vector and estimate feature coordinates and hyper-parameters simultaneously. A Monte Carlo filter is used in state estimation, because adding hyper-parameters to state vector makes state space model nonlinear. Experimental results using synthetic data show that the proposed method can estimate appropriate hyper-parameters for tracking motion and reducing the effect of outliers.