In the scope of gestural action recognition , the size of the feature vector representing movements is in general quite large especially when full body movements are considered. Furthermore, this feature vector evolves during the movement performance so that a complete movement is fully represented by a matrix M of size DxT, whose element (M_ i,j ) represents the value of feature i at timestamps j. Many studies have addressed dimensionality reduction considering only the size of the feature vector lying in ( R ^D ) to reduce both the variability of gestural sequences expressed in the reduced space, and the computational complexity of their processing. In return, very few of these methods have explicitly addressed the dimensionality reduction along the time axis. Yet this is a major issue when considering the use of elastic distances which are characterized by a quadratic complexity along the time axis. We present in this paper an evaluation of straightforward approaches aiming at reducing the dimensionality of the matrix M for each movement, leading to consider both the dimensionality reduction of the feature vector as well as its reduction along the time axis. The dimensionality reduction of the feature vector is achieved by selecting remarkable joints in the skeleton performing the movement, basically the extremities of the articulatory chains composing the skeleton. The temporal dimensionality reduction is achieved using either a regular or adaptive down-sampling that seeks to minimize the reconstruction error of the movements. Elastic and Euclidean kernels are then compared through support vector machine learning. Two data sets that are widely referenced in the domain of human gesture recognition, and quite distinctive in terms of quality of motion capture, are used for the experimental assessment of the proposed approaches. On these data sets we experimentally show that it is feasible, and possibly desirable, to significantly reduce simultaneously the size of the feature vector and the number of skeleton frames to represent body movements while maintaining a very good recognition rate. The method proves to give satisfactory results at a level currently reached by state-of-the-art methods on these data sets. We experimentally show that the computational complexity reduction that is obtained makes this approach eligible for real-time applications.
Gesture recognition Recognition methods essentially aim at modeling the dynamics of gestures. Some approaches, based on linear dynamic models @cite_24 , have used autoregressive (AR) and autoregressive moving-average (ARMA) models to characterize the kinematics of movements, while other approaches, based on nonlinear dynamic models @cite_14 , have developed movement analysis and recognition scheme based on dynamical models controlled by Gaussian processes. @cite_23 propose a synthesis of the major gesture recognition approaches relying on Hidden Markov Models (HMM). Histograms of oriented 4D normals have also been proposed in @cite_13 for the recognition of gestural actions from sequences of depth images. @cite_7 have exploited conditional random fields to model joint dependencies and thus increase the discrimination of HMM-like models. Recurrent neural network models have also been used @cite_35 ; among them, conditional restricted Boltzman's machines @cite_10 have been studied recently in the context of motion captured data modeling and classification.
@cite_35 @cite_14 @cite_7 @cite_10 @cite_24 @cite_23 @cite_13
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This paper describes a novel method called Deep Dynamic Neural Networks (DDNN) for multimodal gesture recognition. A semi-supervised hierarchical dynamic framework based on a Hidden Markov Model (HMM) is proposed for simultaneous gesture segmentation and recognition where skeleton joint information, depth and RGB images, are the multimodal input observations. Unlike most traditional approaches that rely on the construction of complex handcrafted features, our approach learns high-level spatio-temporal representations using deep neural networks suited to the input modality: a Gaussian-Bernouilli Deep Belief Network ( DBN ) to handle skelet al dynamics, and a 3D Convolutional Neural Network ( 3DCNN ) to manage and fuse batches of depth and RGB images. This is achieved through the modeling and learning of the emission probabilities of the HMM required to infer the gesture sequence. This purely data driven approach achieves a Jaccard index score of 0.81 in the ChaLearn LAP gesture spotting challenge. The performance is on par with a variety of state-of-the-art hand-tuned feature-based approaches and other learning-based methods, therefore opening the door to the use of deep learning techniques in order to further explore multimodal time series data. We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorporate hidden structures have proven to be advantageous for recognition tasks. Most existing approaches to gesture recognition with hidden states employ a Hidden Markov Model or suitable variant (e.g., a factored or coupled state model) to model gesture streams; a significant limitation of these models is the requirement of conditional independence of observations. In addition, hidden states in a generative model are selected to maximize the likelihood of generating all the examples of a given gesture class, which is not necessarily optimal for discriminating the gesture class against other gestures. Previous discriminative approaches to gesture sequence recognition have shown promising results, but have not incorporated hidden states nor addressed the problem of predicting the label of an entire sequence. In this paper, we derive a discriminative sequence model with a hidden state structure, and demonstrate its utility both in a detection and in a multi-way classification formulation. We evaluate our method on the task of recognizing human arm and head gestures, and compare the performance of our method to both generative hidden state and discriminative fully-observable models. Existing gesture segmentations use the backward spotting scheme that first detects the end point, then traces back to the start point and sends the extracted gesture segment to the hidden Markov model (HMM) for gesture recognition. This makes an inevitable time delay between the gesture segmentation and recognition and is not appropriate for continuous gesture recognition. To solve this problem, we propose a forward spotting scheme that executes gesture segmentation and recognition simultaneously. The start and end points of gestures are determined by zero crossing from negative to positive (or from positive to negative) of a competitive differential observation probability that is defined by the difference of observation probability between the maximal gesture and the non-gesture. We also propose the sliding window and accumulative HMMs. The former is used to alleviate the effect of incomplete feature extraction on the observation probability and the latter improves the gesture recognition rate greatly by accepting all accumulated gesture segments between the start and end points and deciding the gesture type by a majority vote of all intermediate recognition results. We use the predetermined association mapping to determine the 3D articulation data, which reduces the feature extraction time greatly. We apply the proposed simultaneous gesture segmentation and recognition method to recognize the upper-body gestures for controlling the curtains and lights in a smart home environment. Experimental results show that the proposed method has a good recognition rate of 95.42 for continuously changing gestures. A new method is developed using the hidden Markov model (HMM) based technique. To handle nongesture patterns, we introduce the concept of a threshold model that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture patterns. The threshold model is a weak model for all trained gestures in the sense that its likelihood is smaller than that of the dedicated gesture model for a given gesture. Consequently, the likelihood can be used as an adaptive threshold for selecting proper gesture model. It has, however, a large number of states and needs to be reduced because the threshold model is constructed by collecting the states of all gesture models in the system. To overcome this problem, the states with similar probability distributions are merged, utilizing the relative entropy measure. Experimental results show that the proposed method can successfully extract trained gestures from continuous hand motion with 93.14 reliability.