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1.
提出一种新的基于条件随机域和隐马尔可夫模型(HMM)的人类动作识别方法——HMCRF。目前已有的动作识别方法均使用隐马尔可夫模型及其变型,这些模型一个最突出的不足就是要求观察值相互独立。条件模型很容易表示上下文相关性,且可使用动态规划做到有效且精确的推论,它的参数可以通过凸函数优化训练得到。把条件图形模型应用于动作识别之上,并通过大量的实验表明,所提出的方法在识别正确率方面明显优于一般线性结构的CRF和HMM。  相似文献   

2.
To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model (FBMM) is presented in this paper. FBMM relaxes conditional independence assumptions for classical hidden Markov model (HMM) by adding the specific cross-observation dependencies between observation elements. Compared with buried Markov model (BMM), FBMM utilizes cloud distribution to replace probab...  相似文献   

3.
In this paper we consider two related problems in hidden Markov models (HMMs). One, how the various parameters of an HMM actually contribute to predictions of state sequences and spatio-temporal pattern recognition. Two, how the HMM parameters (and associated HMM topology) can be updated to improve performance. These issues are examined in the context of four different experimental settings from pure simulations to observed data. Results clearly demonstrate the benefits of applying some critical tests on the model parameters before using it as a predictor or spatio-temporal pattern recognition technique.  相似文献   

4.
Recognizing human actions from a stream of unsegmented sensory observations is important for a number of applications such as surveillance and human-computer interaction. A wide range of graphical models have been proposed for these tasks, and are typically extensions of the generative hidden Markov models (HMMs) or their discriminative counterpart, conditional random fields (CRFs). These extensions typically address one of three key limitations in the basic HMM/CRF formalism – unrealistic models for the duration of a sub-event, not encoding interactions among multiple agents directly and not modeling the inherent hierarchical organization of activities. In our work, we present a family of graphical models that generalize such extensions and simultaneously model event duration, multi agent interactions and hierarchical structure. We also present general algorithms for efficient learning and inference in such models based on local variational approximations. We demonstrate the effectiveness of our framework by developing graphical models for applications in automatic sign language (ASL) recognition, and for gesture and action recognition in videos. Our methods show results comparable to state-of-the-art in the datasets we consider, while requiring far fewer training examples compared to low-level feature based methods.  相似文献   

5.
Hidden Markov model (HMM) has made great achievements in many fields such as speech recognition and engineering. However, due to its assumption of state conditional independence between observations, HMM has a very limited capacity for recognizing complex patterns involving more than first-order dependencies in customer relationships management. Group Method of Data Handling (GMDH) could overcome the drawbacks of HMM, so we propose a hybrid model by combining the HMM and GMDH to score customer credit. There are three phases in this model: training HMM with multiple observations, adding GMDH into HMM and optimizing the hybrid model. The proposed hybrid model is compared with other exiting methods in terms of average accuracy, Type I error, Type II error and AUC. Experimental results show that the proposed method has better performance than HMM/ANN in two credit scoring datasets. The implementation of HMM/GMDH hybrid model allows lenders and regulators to develop techniques to measure customer credit risk.  相似文献   

6.
A model-based hand gesture recognition system   总被引:2,自引:0,他引:2  
This paper introduces a model-based hand gesture recognition system, which consists of three phases: feature extraction, training, and recognition. In the feature extraction phase, a hybrid technique combines the spatial (edge) and the temporal (motion) information of each frame to extract the feature images. Then, in the training phase, we use the principal component analysis (PCA) to characterize spatial shape variations and the hidden Markov models (HMM) to describe the temporal shape variations. A modified Hausdorff distance measurement is also applied to measure the similarity between the feature images and the pre-stored PCA models. The similarity measures are referred to as the possible observations for each frame. Finally, in recognition phase, with the pre-trained PCA models and HMM, we can generate the observation patterns from the input sequences, and then apply the Viterbi algorithm to identify the gesture. In the experiments, we prove that our method can recognize 18 different continuous gestures effectively. Received: 19 May 1999 / Accepted: 4 September 2000  相似文献   

7.
基于拉普拉斯脸和隐马尔可夫的视频人脸识别   总被引:1,自引:2,他引:1       下载免费PDF全文
提出了一种基于拉普拉斯脸和隐马尔可夫模型的视频人脸识别方法。在训练过程中,采用拉普拉斯脸方法将每一视频序列中的人脸图像映射到拉普拉斯空间,将降维后的特征作为观测值,通过隐马尔可夫模型得到每一训练视频的统计特性和时间动态特性。在识别过程中,用每一个训练视频的隐马尔可夫模型来分析测试视频的时间动态特性,计算出每一训练模型产生该序列的概率,概率最大值所对应的模型就是待识别序列所属的类别。实验结果表明,该方法能够很好地进行视频人脸识别。  相似文献   

8.
基于HMM的车辆行驶状态实时判别方法研究   总被引:3,自引:1,他引:2  
对交通视频车辆轨迹时序特征下的车辆行驶状态进行研究,提出了一种基于隐马尔科夫模型(Hidden Markov model,HMM)的车辆行驶状态实时判别方法.首先对轨迹序列进行了基于轨迹长度的去不完整轨迹序列、对车辆轨迹点序列的线 性平滑滤波和最小二乘线性拟合的预处理操作,保证了所获得轨迹序列的有效性;其次,提出一种基于车辆运行轨迹点序列方向角的车辆轨迹特征值表示方法和基于方向角区间划分的HMM观察值序列生成方法,该方法以方向角的区间变化来区分不同轨迹模式的特征;最后,采用多观察值序列下的Baum-Welch 算法训练得到相关交通场景轨迹模式类的最优HMM 参数,并通过实时获取车辆行驶轨迹段与相应模型的匹配,实现对车辆行驶状态的实时判别. 仿真实验验证了本文方法的有效性和稳定性.  相似文献   

9.
In the frame of designing a knowledge discovery system, we have developed stochastic models based on high-order hidden Markov models. These models are capable to map sequences of data into a Markov chain in which the transitions between the states depend on the n previous states according to the order of the model. We study the process of achieving information extraction from spatial and temporal data by means of an unsupervised classification. We use therefore a French national database related to the land use of a region, named Ter Uti, which describes the land use both in the spatial and temporal domain. Land-use categories (wheat, corn, forest, ...) are logged every year on each site regularly spaced in the region. They constitute a temporal sequence of images in which we look for spatial and temporal dependencies. The temporal segmentation of the data is done by means of a second-order Hidden Markov Model (HMM2) that appears to have very good capabilities to locate stationary segments, as shown in our previous work in speech recognition. The spatial classification is performed by defining a fractal scanning of the images with the help of a Hilbert–Peano curve that introduces a total order on the sites, preserving the relation of neighborhood between the sites. We show that the HMM2 performs a classification that is meaningful for the agronomists. Spatial and temporal classification may be achieved simultaneously by means of a two levels HMM2 that measures the a posteriori probability to map a temporal sequence of images onto a set of hidden classes.  相似文献   

10.
Standard hidden Markov models (HMM's) have been studied extensively in the last two decades. It is well known that these models assume state conditional independence of the observations. Therefore, they are inadequate for classification of complex and highly structured patterns. Nowadays, the need for new statistical models that are capable to cope with structural time series data is increasing. We propose in this paper a novel paradigm that we named “structural hidden Markov model” (SHMM). It extends traditional HMM's by partitioning the set of observation sequences into classes of equivalences. These observation sequences are related in the sense they all contribute to produce a particular local structure. We describe four basic problems that are assigned to a structural hidden Markov model: (1) probability evaluation, (2) statistical decoding, (3) local structure decoding, and (4) parameter estimation. We have applied SHMM in order to mine customers' preferences for automotive designs. The results reported in this application show that SHMM's outperform the traditional hidden Markov model with a 9% of increase in accuracy. Note In other words, it is possible to decrease the resolution level of a complex pattern.  相似文献   

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