1,237 research outputs found

    Human Activity Recognition System Based-on Sequential Logic Circuits and Statistical Models

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    this research proposed the human activityrecognition system that described complete flow of processes fromlowest process (dealing with images) to highest process (recognizehuman activity). We proposed human action recognition thatmanage image sequence then recognize human action with simplehuman model by model-based recognition technique. Theexperimental result shows good accuracy which up to 93%correctly recognized. We proposed the human activity processwith 3 methods that consecutive improved. All of those methodscan use the result of action recognition as inputs. First method isFSM recognizer. The human model in Finite State Machine (FSM)recognizer can be modeled by rational condition that make it easyto understand and consume low computation cost but it hard todefine complex activity condition so it is unsuitable method forcomplex activity. The second recognizer applied Hidden MarkovModel (HMM) for activity modeling. The HMM recognizer candealing with much more complex activity and give fair recognitionrate. However, HMM recognizer is not involve feature prioritythat should has effect to accuracy so we proposed the thirdrecognizer that used graph similarity measurement for activitymodeling and activity classification. The third one, GraphSimilarity Measurement (GSM) recognizer involved featurepriority for recognition method then show better result thanHMM in most measurement. GSM recognizer has ~84% accuracyin average. FSM recognizer is suitable for simple activity with lowcomputation cost while HMM is suitable for much more complexactivity and use single feature for recognition process. However,HMM method may not give best result for the activity that usemultiple features. GSM is also suitable for complex activity and,furthermore, give better result than HMM for the activity thattrained from multiple features

    Action recognition in depth videos using nonparametric probabilistic graphical models

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    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

    Multioccupant Activity Recognition in Pervasive Smart Home Environments

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    been the center of lot of research for many years now. The aim is to recognize the sequence of actions by a specific person using sensor readings. Most of the research has been devoted to activity recognition of single occupants in the environment. However, living environments are usually inhabited by more than one person and possibly with pets. Hence, human activity recognition in the context of multi-occupancy is more general, but also more challenging. The difficulty comes from mainly two aspects: resident identification, known as data association, and diversity of human activities. The present survey paper provides an overview of existing approaches and current practices for activity recognition in multi-occupant smart homes. It presents the latest developments and highlights the open issues in this field
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