A Complete Recipe for Bayesian Knowledge Transfer: Object Tracking
This work addresses model mismatch in Bayesian transfer learning for object tracking, offering a robust solution for sequential knowledge transfer between tasks, though it appears incremental as it builds on existing Bayesian and MCMC frameworks.
The paper tackles the problem of sequentially transferring knowledge between Bayesian filters for object tracking by introducing a novel Bayesian model that accounts for model-jump, enabling the object to choose and follow different motion models over time. The proposed method, integrated with MCMC, demonstrates advantages in handling model mismatch and robustly estimating trajectories under various motions.
The problem of sequentially transferring from a source object track and a model to another Bayesian filter has become ubiquitous. Due to the lack of a structural model that can capture the dependence among different models, the transfer may not be fully specified. In this paper, we introduce a novel Bayesian model that accounts for the model-jump from which the object can choose a model and follow. We aim to track the trajectory of the object while sequentially transferring from the source object to the target object. The main idea is to impute the dynamical model while tracking the object and estimating the state parameters of the moving object according to discretized dynamic systems. We demonstrate this procedure can handle the model mismatch as it sequentially corrects the predictive model. Particularly, for a fixed number of motion models, the object can learn what motion to follow at each time step. We employ a prior model for each model and then adaptively correct for changing one model to another to robustly estimate object trajectory under various motions. More concretely, we propose a robust Bayesian recipe to handle the model-jump and then integrate it with a Markov chain Monte Carlo (MCMC) approach to sample from the posterior distribution. We demonstrate through experiments the advantage of accounting for model-jump in our proposed method for knowledge transfer between learning tasks in Bayesian transfer learning.