Recognition of human activity through hierarchical stochastic learning

  title={Recognition of human activity through hierarchical stochastic learning},
  author={Sebastian L{\"u}hr and Hung Hai Bui and Svetha Venkatesh and Geoff A. W. West},
  journal={Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003).},
  • Sebastian Lühr, H. Bui, +1 author G. West
  • Published 2003
  • Computer Science
  • Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003).
Seeking to extend the functional capability of the elderly, we explore the use of probabilistic methods to learn and recognise human activity in order to provide monitoring support. We propose a novel approach to learning the hierarchical structure of sequences of human actions through the application of the hierarchical hidden Markov model (HHMM). Experimental results are presented for learning and recognising sequences of typical activities in a home. 
Efficient duration and hierarchical modeling for human activity recognition
This paper exploits efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and applies it to the problem of learning and recognizing ADLs with complex temporal dependencies, demonstrating that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. Expand
Hierarchical-HMM Based Recognition of Human Activity
The hierarchical hidden Markov model (HHMM) is employed to represent the models, in which each model is composed of a submodel of the target action and submodels which can represent any action, and they are connected appropriately. Expand
Hierarchical Activity Recognition Using Automatically Clustered Actions
This paper presents a two-layer hierarchical model in which activities consist of a sequence of actions, which outperforms the non-hierarchical models in all datasets and does so significantly in two of the three datasets. Expand
Learning Hierarchical Models of Complex Daily Activities from Annotated Videos
A novel approach for building models of complex long-term activities by automatically learning the hierarchical structure of activities by learning about the 'parent-child' relation of activity components from a video using the variability in annotations acquired using multiple annotators. Expand
Activity recognition and abnormality detection with the switching hidden semi-Markov model
The switching hidden semi-markov model (S-HSMM) is introduced, a two-layered extension of thehidden semi-Markov model for the modeling task and an effective scheme to detect abnormality without the need for training on abnormal data is proposed. Expand
A weakly supervised activity recognition framework for real-time synthetic biology laboratory assistance
We describe the design of a hybrid system -- a combination of a Dynamic Graphical Model (DGM) with a Deep Neural Network (DNN) -- to identify activities performed during synthetic biologyExpand
Identification and prediction of abnormal behaviour activities of daily living in intelligent environments
The aim of this research is to investigate efficient mining of useful information from a sensor network forming an Ambient Intelligence (AmI) environment. In this thesis, we investigate methods forExpand
Identifying Tasks and Predicting Action in Smart Homes using Unlabeled Data
This paper model inhabitant actions as states in a simple Markov model, and introduces an enhancement, the TaskbasedMarkov model (TMM) method, which discovers high-level inhabitant tasks using the unlabeled data supplied. Expand
Duration Abnormality Detection in Sequences of Human Activity
Activity duration is an essential element in the accurate modelling of human behaviour. The application of a standard hidden Markov Model (HMM) for the detection of abnormality in sequences of humanExpand
PREDIcting inhabitant action using action and task models with application to smart homes
This paper model inhabitant actions as states in a simple Markov model, and introduces an enhancement to this basic approach, the Task-basedMarkov model (TMM) method, which discovers high-level inhabitant tasks using the supplied unlabeled data. Expand


Recognition of Visual Activities and Interactions by Stochastic Parsing
A probabilistic syntactic approach to the detection and recognition of temporally extended activities and interactions between multiple agents and how the system correctly interprets activities of multiple interacting objects is demonstrated. Expand
A Bayesian Computer Vision System for Modeling Human Interactions
A real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task and demonstrates the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training. Expand
Hierarchical monitoring of people's behaviors in complex environments using multiple cameras
The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. Expand
Policy Recognition in the Abstract Hidden Markov Model
This paper introduces the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network, and proposes a novel plan recognition framework based on the AHMM as the plan execution model. Expand
Learning Patterns of Activity Using Real-Time Tracking
This paper focuses on motion tracking and shows how one can use observed motion to learn patterns of activity in a site and create a hierarchical binary-tree classification of the representations within a sequence. Expand
The Hierarchical Hidden Markov Model: Analysis and Applications
This work introduces, analyzes and demonstrates a recursive hierarchical generalization of the widely used hidden Markov models, which is motivated by the complex multi-scale structure which appears in many natural sequences, particularly in language, handwriting and speech. Expand
Hierarchical learning and planning in partially observable markov decision processes
A Hierarchical POMDP (HPOMDP) model to scale learning and planning to large scale partially observable environments and the results show that the planning algorithms are very successful in taking the robot to any environment state starting from no positional knowledge, and use significantly less number of steps than flat approaches. Expand
Tracking and Surveillance in Wide-Area Spatial Environments Using the Abstract Hidden Markov Model
This paper describes an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail. Expand
A Tutorial on Hidden Markov Models and Selected Applications
The fabric comprises a novel type of netting which will have particular utility in screening out mosquitoes and like insects and pests. The fabric is defined of voids having depth as well as widthExpand
A New Approach to Linear Filtering and Prediction Problems
The clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the ?stat-tran-sition? method of analysis of dynamic systems. New resultExpand