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Spatio-temporal pattern recognition using hidden Markov models

机译：使用隐马尔可夫模型的时空模式识别

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摘要

A spatio-temporal method for identifying objects contained in an image sequence is presented. The Hidden Markov Model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. A five class problem is considered. Classification accuracies of 100% and 99.7%, are obtained for sequences of images generated over two separate regions of viewing positions. HMMs trained on image sequences of the objects moving in opposite directions showed a 98.1% successful classification rate by class and direction of movement. The HMM technique proved robust to image corruption with additive correlated noise and had a higher accuracy than a single-look nearest neighbor method. A real image sequence of one of the objects used was successfully recognized with the HMMs trained on synthetic data. This study shows the temporal changes that observed feature vectors undergo due to object motion hold information that can yield superior classification accuracy when compared with single-frame techniques
机译：提出了一种用于识别图像序列中所包含对象的时空方法。隐马尔可夫模型（HMM）技术用作分类算法，根据观察到的目标特征的时空序列进行分类决策。考虑了五类问题。对于在两个单独的观看位置区域上生成的图像序列，可获得100％和99.7％的分类精度。在沿相反方向运动的对象的图像序列上训练的HMM按运动的类别和方向显示98.1％的成功分类率。 HMM技术经证明具有加性相关噪声对图像破坏的鲁棒性，并且比单看最近邻居方法具有更高的准确性。通过对合成数据进行训练的HMM，成功识别了所用对象之一的真实图像序列。这项研究表明，与单帧技术相比，观察到的特征向量由于对象运动保持信息而经历的时间变化可产生更高的分类精度

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