Tracking multiple moving objects in images using Markov Chain Monte Carlo
Tracking multiple moving objects in images using Markov Chain Monte Carlo
复制标题
使用马尔可夫链蒙特卡罗跟踪图像中的多个移动对象
DOI：
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发表时间：
2016
影响因子：
2.2
通讯作者：
Sumeetpal S. Singh
中科院分区：
文献类型：
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作者：
Lan Jiang;Sumeetpal S. Singh
提出了一种新的基于图像观测的多目标跟踪模型的贝叶斯状态和参数学习算法。具体而言，马尔可夫链蒙特卡罗算法的设计，从后验分布的未知的时变数量的目标，他们的出生，死亡时间和状态以及模型参数，这构成了完整的解决方案，我们考虑的特定跟踪问题。传统的方法是对图像进行预处理以提取点观测，然后执行跟踪，即推断目标轨迹。我们直接对图像生成过程进行建模，以避免在使用与推理算法解耦的预处理步骤提取点观测时出现任何潜在的信息丢失。数值例子表明，我们的算法有改进的跟踪性能比常用的技术，合成的例子和真实的荧光显微镜数据，特别是在昏暗的目标重叠照明区域的情况下。
A new Bayesian state and parameter learning algorithm for multiple target tracking models with image observations are proposed. Specifically, a Markov chain Monte Carlo algorithm is designed to sample from the posterior distribution of the unknown time-varying number of targets, their birth, death times and states as well as the model parameters, which constitutes the complete solution to the specific tracking problem we consider. The conventional approach is to pre-process the images to extract point observations and then perform tracking, i.e. infer the target trajectories. We model the image generation process directly to avoid any potential loss of information when extracting point observations using a pre-processing step that is decoupled from the inference algorithm. Numerical examples show that our algorithm has improved tracking performance over commonly used techniques, for both synthetic examples and real florescent microscopy data, especially in the case of dim targets with overlapping illuminated regions.