Distributed multi-dimensional hidden Markov model: theory and application in multiple-object trajectory classification and recognition
Distributed multi-dimensional hidden Markov model: theory and application in multiple-object trajectory classification and recognition
复制标题
分布式多维隐马尔可夫模型：多目标轨迹分类与识别的理论与应用
DOI：
10.1117/12.766004
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
A. Khokhar
中科院分区：
文献类型：
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作者：
Xiang Ma;D. Schonfeld;A. Khokhar
本文提出了一种新的分布式因果多维隐马尔可夫模型。例如，该模型可以表示对象的多个运动轨迹及其在场景中的交互活动；它不仅能够传达每个轨迹的动力学，而且能够传达多个轨迹之间的交互信息，这在许多应用中可能是至关重要的。我们首先提出了一种非因果多维隐马尔可夫模型(HMM)的解决方案，将非因果模型分布到多个分布式的因果HMM中。我们通过一种交替的更新方案来近似求解顺序处理器上的多个隐马尔可夫模型的同时解。随后，我们给出了三种算法对我们提出的模型进行训练和分类。推导了一种适用于新模型估计的新的期望最大化(EM)算法，其中提出了一种新的通用前向后向(GFB)算法来递推估计模型参数。针对二维维特比算法，提出了一种新的状态序列的条件独立子集-状态序列结构分解方法。该模型还可应用于图像分割、图像分类等领域。在多个交互轨迹分类中的仿真结果表明，与以前的模型相比，该分布式隐马尔可夫模型具有更好的性能和更高的准确率。
In this paper, we propose a novel distributed causal multi-dimensional hidden Markov model (DHMM). The proposed model can represent, for example, multiple motion trajectories of objects and their interaction activities in a scene; it is capable of conveying not only dynamics of each trajectory, but also interactions information between multiple trajectories, which can be critical in many applications. We firstly provide a solution for non-causal, multi-dimensional hidden Markov model (HMM) by distributing the non-causal model into multiple distributed causal HMMs. We approximate the simultaneous solution of multiple HMMs on a sequential processor by an alternate updating scheme. Subsequently we provide three algorithms for the training and classification of our proposed model. A new Expectation-Maximization (EM) algorithm suitable for estimation of the new model is derived, where a novel General Forward-Backward (GFB) algorithm is proposed for recursive estimation of the model parameters. A new conditional independent subset-state sequence structure decomposition of state sequences is proposed for the 2D Viterbi algorithm. The new model can be applied to many other areas such as image segmentation and image classification. Simulation results in classification of multiple interacting trajectories demonstrate the superior performance and higher accuracy rate of our distributed HMM in comparison to previous models.