Psychology Essay 代写: Identification Of Posture Prototypes

Psychology Essay 代写: Identification Of Posture Prototypes

从视频序列的人类行动的识别有许多应用在视频监控和监控[ 2 ]，人机交互[ 3 ]，基于内容的视频检索[ 3 ]，体育赛事和更多的分析。术语行动是指一段时间内的人类活动模式的一个周期。行动是区别于活动。活动是一个连续的小原子动作的事件。例如活动慢跑由以下行动走，跑，跳等认识人类行为是一个非常具有挑战性的问题，因为动作看起来不同取决于语境等相同的动作，不同的服装，同样的动作在不同的观点和不同的人不同的人进行相同的动作，但它可能会出现以各种方式[ 1 ]。

人的行为表现是用来匹配所有人体的相似性构成的一个自组织映射（SOM）神经网络中。在训练阶段SOM是用来产生行动的独立姿态的原型。在测试阶段自适应神经模糊推理系统是用来分类的最有可能的动作类型的每个测试序列，根据建立在训练阶段的模型。它利用模糊逻辑规则的自动识别和调整的隶属函数[ 5 ]，提出了模糊推理系统的动作分类。模糊推理系统可以被训练，以制定模糊规则，并确定系统的输入和输出变量的隶属函数。贝叶斯框架是识别未知的行动，也产生具有较高的分类精度的组合识别结果[ 6 ]。

S. Ali和M. Shah提出了行为识别的运动学特征。它代表了复杂的人类行动的视频。不同视角下的同一动作，非观不变的运动特征。闭塞也会影响行动的表现[ 7 ]。H. J. Seo和米兰远提出的回归核分析。它捕获的数据，即使在存在的错误陈述的行动和错误的数据。它也发现的相似性的行动，不需要事先了解行动的知识[ 4 ]。M. Ahmad和S. Lee提出的Hidden Markov模型。该方法基于视图独立的人体动作识别，使用人体轮廓特征，光流特征，并结合特征。基于这个特征，我们可以从任意的视图中识别的动作，而不是特定的视图[ 8 ]。

N. Gkalelis等人提出的模糊矢量量化（FVQ）和线性判别分析（LDA）。这种方法允许查看独立的运动识别，而不使用校准相机和不同的动作表示和分类。LDA减少多视点视频运动特征的维数。这种方法是有效的，低维的特征有较好的识别率。它在一个类的对象或事件[ 9 ]发现只有特征的线性组合。F. Lv和R. Nevatia提出金字塔匹配内核算法，提高了匹配两个相似特征集之间，达到类似的结果和较低的计算成本，已应用于目标识别。但是，单视图的动作分类需要大量的参数来解决模糊性的分类[ 10 ]。

S. Yu等人提出的基于步态识别的外观。稳健的步态识别系统，它是有价值的。这种方法是不适合于从侧面识别人体动作，也从不同的视角[ 11 ] D.温兰等人提出的主成分分析（PCA），它通常被用于降低高维特征低维特征。它是有用的，用于查看不变识别较大的一类原始动作，它不执行线性分离和线性回归的类，它不执行类似的人类行动也[ 8 ]。

Psychology Essay 代写: Identification Of Posture Prototypes

Recognition of human actions from video sequences has many applications in the video surveillance and monitoring [2], human-computer interaction [3], content based video retrieval [3], analysis of sports events and more. The term action refers to a single period of human motion pattern for a period of time. Action is discriminate from activity. An activity is continuous event of small atomic actions. For example the activity jogging consists of the following actions walk, run, jump etc. Recognizing the human action is a very challenging problem because the actions can look very different depending upon the context such as same actions with different garbs, same action performed by different people in different viewpoints or different people performed the same action but it may appear in various way [1].

Representation of human action is used to match the similarity of all human body poses by a self organizing map (SOM) in a neural network. In the training phase SOM is used to produce action independent posture prototypes. In the testing phase Adaptive neuro fuzzy inference system is used to classify each testing sequence of the most probable action type according to the model built in the training phase. It utilizes automatic identification of fuzzy logic rules and adjusts the membership function [5].Fuzzy inference system is proposed for the action classification. Fuzzy inference system can be trained to develop fuzzy rules and determine membership functions for input and output variables of the system. Bayesian Framework is to recognize the unknown actions and also produces the combined recognition results with high classification accuracy [6].

S. Ali and M. Shah proposed kinematic features for Action recognition. It represents the complex human action in videos. Kinematic features not view invariant because the same action viewed by different viewing angle. Occlusion will also affect the performance of the action [7]. H. J. Seo and P. Milan far proposed regression kernel analysis. It captures the data even in the presence of misrepresentation of action and error present in the data. It also finds the similarity actions and does not need prior knowledge about actions [4]. M. Ahmad and S. Lee proposed Hidden Markov Model. This method based on view-independent human action recognition using body silhouette feature, optical flow feature, and combined feature. Based on this feature, we can recognize the action from arbitrary view rather than the specific view [8].

N.Gkalelis et al proposed fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA). This method allows view independent movement recognition, without the use of calibrated cameras and different movements are represented and classified. LDA reduces the dimensionality of the multiview movement video features.This method is efficient because low dimensional feature achieves good recognition rates.It finds only the linear combination of features in a classes of objects or events [9]. F. Lv and R. Nevatia proposed Pyramid Match Kernel algorithm.It improves the matching score between two similar feature sets.It achieves comparable result and lower computational cost.It has been applied to object recognition. But the single view action classification need large number of parameter to solve the ambiguity of the classification [10].

S. Yu et al proposed appearance based gait recognition.It is valuable for robust gait recognition system.This method is not suitable to recognize the human action from side view and also from various viewing angles [11].D. Weinland et al proposed principal component analysis (PCA).It is commonly used to reduce the higher dimensional features into lower dimensional features. It is useful for view invariant recognition for larger class of primitive actions.It does not perform linear separation and linear regression of classes and it does not perform the similar human actions also [8].