论文标题
贝叶斯知识转移的完整食谱：对象跟踪
A Complete Recipe for Bayesian Knowledge Transfer: Object Tracking
论文作者
论文摘要
从源对象轨道和模型依次转移到另一个贝叶斯滤波器的问题已变得无处不在。由于缺乏可以捕获不同模型之间依赖性的结构模型，因此可能无法完全指定转移。在本文中，我们介绍了一种新颖的贝叶斯模型，该模型解释了对象可以从中选择模型并遵循的模型。我们旨在跟踪对象的轨迹，同时从源对象顺序传输到目标对象。主要思想是在跟踪对象并根据离散的动态系统来估算移动对象的状态参数时进行动态模型。我们证明此过程可以处理模型不匹配，因为它依次纠正预测模型。特别是，对于固定数量的运动模型，该对象可以在每个时间步骤中学习要遵循的运动。我们对每个模型采用先前的模型，然后自适应地正确将一个模型更改为另一个模型以在各种动作下稳健地估算对象轨迹。更具体地说，我们提出了一种健壮的贝叶斯配方，以处理模型跳跃，然后将其与马尔可夫链蒙特卡洛（MCMC）方法集成在一起，以从后分布中采样。我们通过实验证明，在我们提出的方法中，在贝叶斯转移学习中学习任务之间的知识转移的方法中，对模型跳跃的优势进行了证明。
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.
