Vision based Traffic Police Hand Signal Recognition in Surveillance Video- A Survey R.Sathya <s> Action <s> We address the problem of detecting irregularities in visual data, e.g., detecting suspicious behaviors in video sequences, or identifying salient patterns in images. The term "irregular" depends on the context in which the "regular" or "valid" are defined. Yet, it is not realistic to expect explicit definition of all possible valid configurations for a given context. We pose the problem of determining the validity of visual data as a process of constructing a puzzle: We try to compose a new observed image region or a new video segment ("the query") using chunks of data ("pieces of puzzle") extracted from previous visual examples ("the database "). Regions in the observed data which can be composed using large contiguous chunks of data from the database are considered very likely, whereas regions in the observed data which cannot be composed from the database (or can be composed, but only using small fragmented pieces) are regarded as unlikely/suspicious. The problem is posed as an inference process in a probabilistic graphical model. We show applications of this approach to identifying saliency in images and video, and for suspicious behavior recognition. <s> BIB001 </s> Vision based Traffic Police Hand Signal Recognition in Surveillance Video- A Survey R.Sathya <s> Action <s> In this paper we discuss how a Gesture Recognition system for Alphabetical Hand Gestures is built. The main motive was to develop a system that can simplify the way humans interact with computers. The system is designed using the Support Vector Machine (SVM) Classifier which is widely used for classification and regression testing. SVM training algorithm builds a model that predicts whether a new example falls into one category or other. And the SVM classifier learns from the data points in examples when they are classified belonging to their respective categories. <s> BIB002
Recognition using the HMM. In Markov Model (MM) the state is directly visible to the observer, so the state transition probability is the only parameter. The Hidden Markov Model (HMM) models are sequence of observations as a piecewise stationary process. In hidden markov model the states are not directly accessible to the observer. Each state has probability distribution over output tokens. The sequence of tokens generated by an HMM gives some information about the sequence of state. Hidden variable controls the components to be selected for each observation. The HMM is stochastic approach which models the given problem as a "doubly stochastic process" in which the observed data are thought to be the result of having passed the hidden processes are to be characterized using only the one that could be observed. Two types of HMM model. The Hidden Markov Models are a popular technique for recognizing human gesture in a varity of applicatisons and sensor configuration. Hidden Markov Models are double stochastic process as governed by an underlying Markov chain with a finite number of states, and a set of random functions each of which is associated with one state BIB001 . The following is an illustrative list of applications of HMM: Speech recognition Recognition using the KNN. K-nearest neighbors (KNN) classifiers have a good performance when the attributes of a system are linearly separable. The class which has the most vectors in those K neighbors is chosen to be the class of the input vector. A cluster is a collection of objects which are similar between them and are dissimilar to the objects belonging to other clusters. Clustering is an unsupervised learning method which deals with finding a structure in a collection of unlabeled data. A loose definition of clustering could be the process of organizing objects into groups whose members are similar in some way. k-means clustering   is an algorithm to group objects based on attributes/features into k number of groups where k is a positive integer. The rouping (clustering) is done by minimizing the Euclidean distance between data and the corresponding cluster centroid. Thus the purpose of k-means clustering is to cluster the data. They calculate the distance between cluster centroid to each object using Euclidean distance measure. E-M algorithm   finds out maximum likelihood estimates of parameters in probabilistic models. This algorithm iterates between the E-step and the Mstep until convergence. Expectation step computes an expectation of the likelihood assuming parameters. Maximization step computes maximum likelihood estimates of parameters by maximizing the expected likelihood found in E-step. K-nearest Neighbors With Distance Weighting (KNNDW) is an improvement which has been proved to perform better than KNN in many cases BIB002 . In this method, the contribution of each neighbor to the overall classification is weighted by its distance from the point being classified. 6.2.4 Action Recognition using the NN. Artificial Neural Network (ANN) is an information processing system that is inspired by biological nervous system like the brain process information. A neural network is a machine that is designed to model the way in which the brain performs a particular task or functions. The network is usually implemented by using electronic components or simulated in software on a digital computer. To achieve good performance, neural networks employs massive interconnections of simple computing cells referred to as "neurons" or "processing units". Gesture recognition is an important for developing alternative human-computer interaction modalities using ANN. Backpropogation Neural Network (BPNN) is a multi layer feed forward neural network. BPNN structure is input layer, Hidden layer and output layer. Probabilistic neural network (PNN) is a feedforward neural network that implements a Bayesian decision strategy for classifying input vectors. Artificial Neural networks are flexible in a changing environment  and it also describes the process of gesture recognition using ANN  . Probabilistic Neural Network (PNN) is a feed-forward neural network that implements a Bayesian decision strategy for classifying input vectors. Neural network models such as Backpropagation Neural Network (BPNN)  and radial basis function neural network (RBFNN) are used for pattern classification because of their ability to capture the nonlinear hyperspace separating the classes in the feature space. A special kind of backpropagation neural network called Autoassociative Neural Network (AANN)  can be used to capture the distribution of feature vectors in the feature space.