Activity recognition using a supervised non-parametric hierarchical HMM

The problem of classifying human activities occurring in depth image sequences is addressed. The 3D joint positions of a human skeleton and the local depth image pattern around these joint positions define the features. A two level hierarchical Hidden Markov Model (H-HMM), with independent Markov chains for the joint positions and depth image pattern, is used to model the features. The states corresponding to the H-HMM bottom level characterize the granular poses while the top level characterizes the coarser actions associated with the activities. Further, the H-HMM is based on a Hierarchical Dirichlet Process (HDP), and is fully non-parametric with the number of pose and action states inferred automatically from data. This is a significant advantage over classical HMM and its extensions. In order to perform classification, the relationships between the actions and the activity labels are captured using multinomial logistic regression. The proposed inference procedure ensures alignment of actions from activities with similar labels. Our construction enables information sharing, allows incorporation of unlabelled examples and provides a flexible factorized representation to include multiple data channels. Experiments with multiple real world datasets show the efficacy of our classification approach

Joint segmentation of multivariate time series with hidden process regression for human activity recognition

The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the variability of the human activities over time (dynamic models) rather than static ones which can have some limitations in such a dynamic context. In this paper, the problem of activity recognition is analyzed through the segmentation of the multidimensional time series of the acceleration data measured in the 3-d space using body-worn accelerometers. The proposed model for automatic temporal segmentation is a specific statistical latent process model which assumes that the observed acceleration sequence is governed by sequence of hidden (unobserved) activities. More specifically, the proposed approach is based on a specific multiple regression model incorporating a hidden discrete logistic process which governs the switching from one activity to another over time. The model is learned in an unsupervised context by maximizing the observed-data log-likelihood via a dedicated expectation-maximization (EM) algorithm. We applied it on a real-world automatic human activity recognition problem and its performance was assessed by performing comparisons with alternative approaches, including well-known supervised static classifiers and the standard hidden Markov model (HMM). The obtained results are very encouraging and show that the proposed approach is quite competitive even it works in an entirely unsupervised way and does not requires a feature extraction preprocessing step

Action classification using a discriminative multilevel HDP-HMM

We classify human actions occurring in depth image sequences using features based on skeletal joint positions. The action classes are represented by a multi-level Hierarchical Dirichlet Process – Hidden Markov Model (HDP-HMM). The non-parametric HDP-HMM allows the inference of hidden states automatically from training data. The model parameters of each class are formulated as transformations from a shared base distribution, thus promoting the use of unlabelled examples during training and borrowing information across action classes. Further, the parameters are learnt in a discriminative way. We use a normalized gamma process representation of HDP and margin based likelihood functions for this purpose. We sample parameters from the complex posterior distribution induced by our discriminative likelihood function using elliptical slice sampling. Experiments with two different datasets show that action class models learnt using our technique produce good classification results

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models

Robot introspection, as opposed to anomaly detection typical in process monitoring, helps a robot understand what it is doing at all times. A robot should be able to identify its actions not only when failure or novelty occurs, but also as it executes any number of sub-tasks. As robots continue their quest of functioning in unstructured environments, it is imperative they understand what is it that they are actually doing to render them more robust. This work investigates the modeling ability of Bayesian nonparametric techniques on Markov Switching Process to learn complex dynamics typical in robot contact tasks. We study whether the Markov switching process, together with Bayesian priors can outperform the modeling ability of its counterparts: an HMM with Bayesian priors and without. The work was tested in a snap assembly task characterized by high elastic forces. The task consists of an insertion subtask with very complex dynamics. Our approach showed a stronger ability to generalize and was able to better model the subtask with complex dynamics in a computationally efficient way. The modeling technique is also used to learn a growing library of robot skills, one that when integrated with low-level control allows for robot online decision making.Comment: final version submitted to humanoids 201

Action recognition in depth videos using nonparametric probabilistic graphical models

Action recognition involves automatically labelling videos that contain human motion with action classes. It has applications in diverse areas such as smart surveillance, human computer interaction and content retrieval. The recent advent of depth sensing technology that produces depth image sequences has offered opportunities to solve the challenging action recognition problem. The depth images facilitate robust estimation of a human skeleton’s 3D joint positions and a high level action can be inferred from a sequence of these joint positions. A natural way to model a sequence of joint positions is to use a graphical model that describes probabilistic dependencies between the observed joint positions and some hidden state variables. A problem with these models is that the number of hidden states must be fixed a priori even though for many applications this number is not known in advance. This thesis proposes nonparametric variants of graphical models with the number of hidden states automatically inferred from data. The inference is performed in a full Bayesian setting by using the Dirichlet Process as a prior over the model’s infinite dimensional parameter space. This thesis describes three original constructions of nonparametric graphical models that are applied in the classification of actions in depth videos. Firstly, the action classes are represented by a Hidden Markov Model (HMM) with an unbounded number of hidden states. The formulation enables information sharing and discriminative learning of parameters. Secondly, a hierarchical HMM with an unbounded number of actions and poses is used to represent activities. The construction produces a simplified model for activity classification by using logistic regression to capture the relationship between action states and activity labels. Finally, the action classes are modelled by a Hidden Conditional Random Field (HCRF) with the number of intermediate hidden states learned from data. Tractable inference procedures based on Markov Chain Monte Carlo (MCMC) techniques are derived for all these constructions. Experiments with multiple benchmark datasets confirm the efficacy of the proposed approaches for action recognition