| 摘要: |
| 提出了一种基于半监督学习的行为建模与异常检测方法.该算法包括以下几个主要步骤:(1) 通过基于动态时间归整(DTW)的谱聚类方法获取适量的正常行为样本,对正常行为的隐马尔可夫模型(HMM)进行初始化;(2) 通过迭代学习的方法在大样本下进一步训练这些隐马尔可夫模型参数;(3) 以监督的方式,利用最大后验(MAP)自适应方法估计异常行为的隐马尔可夫模型参数;(4) 建立行为的隐马尔可夫拓扑结构模型,用于异常检测.该方法的主要特点是:能够自动地选择正常行为模式的种类和样本以建立正常行为模型;能够在较少样本的情 |
| 关键词: 行为建模 异常检测 半监督学习 隐马尔可夫模型 计算机视觉 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.60303021(国家自然科学基金);the National High-Tech Research and Development Plan of China under Grant No.2005AA118020(国家高技术研究发展计划(863)) |
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| Behavior Modeling and Abnormality Detection Based on Semi-Supervised Learning Method |
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LI He-Ping,HU Zhan-Yi,WU Yi-Hong,WU Fu-Chao
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| Abstract: |
| A simple and efficient method based on semi-supervised learning technique is proposed for behavior modeling and abnormality detection. The method is composed of the following steps: (1) Dynamic time warping (DTW) based spectral clustering method is used to obtain a small set of samples to initialize the hidden Markov models (HMMs) of normal behaviors; (2) The HMMs’ parameters are further trained by the method of iterative learning from a large data set; (3) Maximum a posteriori (MAP) adaptation technique is used to estimate the HMMs’ parameters of abnormal behaviors from those of normal behaviors; (4) The topological structure of HMM is finally constructed to detect abnormal behaviors. The main characteristic of the proposed method is that it can automatically select the number of normal behavior patterns and samples from the training dataset to build normal behavior models and can effectively avoid the running risk of over-fitting when the HMMs of abnormal behaviors are learned from sparse data. Experimental results demonstrate the effectiveness of the proposed method in comparison with other related works in the literature. |
| Key words: behavior modeling abnormality detection semi-supervised learning HMM (hidden Markov models) computer vision |
