Performance evaluation of 2D feature tracking based on Bayesian estimation

Feature tracking methods based on Bayesian estimation are widely studied in computer vision systems. The performance of Bayesian decision, however, remains an open problem because an implementation of Bayesian estimation is significantly affected by many parameters in modeling the prior and observation probabilities. In this paper, we evaluate the performance of our MAP based feature tracking algorithm with various parameter settings for many features. For most 2D feature points in our experiments, we found that the uniform distribution model (or Gaussian model with a very large variance) with linear prediction yields the best feature tracking performance.