Extended RBM to model spatio-temporal patterns among high-dimensional motion data.Generative approach to perform classification using RBM, for both binary and multi-class classification.High classification accuracy in two computer vision applications: facial expression recognition and human action recognition. Many computer vision applications involve modeling complex spatio-temporal patterns in high-dimensional motion data. Recently, restricted Boltzmann machines (RBMs) have been widely used to capture and represent spatial patterns in a single image or temporal patterns in several time slices. To model global dynamics and local spatial interactions, we propose to theoretically extend the conventional RBMs by introducing another term in the energy function to explicitly model the local spatial interactions in the input data. A learning method is then proposed to perform efficient learning for the proposed model. We further introduce a new method for multi-class classification that can effectively estimate the infeasible partition functions of different RBMs such that RBM is treated as a generative model for classification purpose. The improved RBM model is evaluated on two computer vision applications: facial expression recognition and human action recognition. Experimental results on benchmark databases demonstrate the effectiveness of the proposed algorithm.
Model-based methods include probabilistic graphical models such as Hidden Markov Models @cite_17 , Dynamic Bayesian Networks @cite_28 , Conditional Random Fields @cite_23 , and their variants. While capable of simultaneously capturing both spatial and temporal interactions, they can only capture the local spatial and temporal interactions due to the underlying Markov assumption.
@cite_28 @cite_23 @cite_17
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We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver. Understanding natural human activity involves not only identifying the action being performed, but also locating the semantic elements of the scene and describing the person's interaction with them. We present a system that is able to recognize complex, fine-grained human actions involving the manipulation of objects in realistic action sequences. Our method takes advantage of recent advances in sensors and pose trackers in learning an action model that draws on successful discriminative techniques while explicitly modeling both pose trajectories and object manipulations. By combining these elements in a single model, we are able to simultaneously recognize actions and track the location and manipulation of objects. To showcase this ability, we introduce a novel Cooking Action Dataset that contains video, depth readings, and pose tracks from a Kinect sensor. We show that our model outperforms existing state of the art techniques on this dataset as well as the VISINT dataset with only video sequences. Our goal is to automatically segment and recognize basic human actions, such as stand, walk and wave hands, from a sequence of joint positions or pose angles. Such recognition is difficult due to high dimensionality of the data and large spatial and temporal variations in the same action. We decompose the high dimensional 3-D joint space into a set of feature spaces where each feature corresponds to the motion of a single joint or combination of related multiple joints. For each feature, the dynamics of each action class is learned with one HMM. Given a sequence, the observation probability is computed in each HMM and a weak classifier for that feature is formed based on those probabilities. The weak classifiers with strong discriminative power are then combined by the Multi-Class AdaBoost (AdaBoost.M2) algorithm. A dynamic programming algorithm is applied to segment and recognize actions simultaneously. Results of recognizing 22 actions on a large number of motion capture sequences as well as several annotated and automatically tracked sequences show the effectiveness of the proposed algorithms.