A Comparison of HMMs and Dynamic Bayesian Networks for Recognizing Office Activities

Oliver, Núria; Horvitz, Eric

doi:10.1007/11527886_26

Cited by 59 publications

(31 citation statements)

References 14 publications

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“…Currently, there are many mathematical models for activity recognition, such as HMMs (Rabiner, 1989), Bayesian Networks (Oliver and Horvitz, 2005), Kalman Filters (Bodor et al, 2003) and Neural Networks (Bodor et al, 2003). Deep learning approaches on RGB video streams for activity recognition have also been introduced.…”

Section: Related Workmentioning

confidence: 99%

HMM-based Activity Recognition with a Ceiling RGB-D Camera

Liciotti

Frontoni

Zingaretti

et al. 2017

Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods

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Abstract:Automated recognition of Activities of Daily Living allows to identify possible health problems and apply corrective strategies in Ambient Assisted Living (AAL). Activities of Daily Living analysis can provide very useful information for elder care and long-term care services. This paper presents an automated RGB-D video analysis system that recognises human ADLs activities, related to classical daily actions. The main goal is to predict the probability of an analysed subject action. Thus, abnormal behaviour can be detected. The activity detection and recognition is performed using an affordable RGB-D camera. Human activities, despite their unstructured nature, tend to have a natural hierarchical structure; for instance, generally making a coffee involves a three-step process of turning on the coffee machine, putting sugar in cup and opening the fridge for milk. Action sequence recognition is then handled using a discriminative Hidden Markov Model (HMM). RADiaL, a dataset with RGB-D images and 3D position of each person for training as well as evaluating the HMM, has been built and made publicly available.

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Section: Related Workmentioning

confidence: 99%

HMM-based Activity Recognition with a Ceiling RGB-D Camera

Liciotti

Frontoni

Zingaretti

et al. 2017

Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods

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“…The main probabilistic approaches that have been used to recognize video events include Bayesian classifiers [4] and Hidden Markov Models [5,6]. Bayesian classifiers are well adapted to combine observations at one time point, but they have not a specific mechanism to represent the time and temporal constraints between visual observations.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Recognition of Complex Event

Romdhane

Boulay

Brémond

et al. 2011

Lecture Notes in Computer Science

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Abstract. This paper describes a complex event recognition approach with probabilistic reasoning for handling uncertainty. The first advantage of the proposed approach is the flexibility of the modeling of composite events with complex temporal constraints. The second advantage is the use of probability theory providing a consistent framework for dealing with uncertain knowledge for the recognition of complex events. The experimental results show that our system can successfully improve the event recognition rate. We conclude by comparing our algorithm with the state of the art and showing how the definition of event models and the probabilistic reasoning can influence the results of the real-time event recognition.

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“…This section describes several of these approaches and a short discussion on the remaining open issues. The main probabilistic approaches that have been used to recognize video events include neural networks [1,2], Bayesian classifier [13] and Hidden Markov Models (HMM) [6,9,12]. The two first approaches (i.e.…”

Section: State Of the Artmentioning

confidence: 99%

A Framework Dealing with Uncertainty for Complex Event Recognition

Romdhane

Brémond

Thonnat

2010

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance

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This paper presents a constraint-based approach for video event recognition with probabilistic reasoning for handling uncertainty. The main advantage of constraintbased approaches is the possibility for human expert to model composite events with complex temporal constraints. But the approaches are usually deterministic and do not enable the convenient mechanism of probability reasoning to handle the uncertainty. The first advantage of the proposed approach is the ability to model and recognize composite events with complex temporal constraints. The second advantage is that probability theory provides a consistent framework for dealing with uncertain knowledge for a robust and reliable recognition of complex event. This approach is evaluated with 4 real healthcare videos and a public video ETISEO'06. The results are compared with state of the art method. The comparison shows that the proposed approach improves significantly the process of recognition and characterizes the likelihood of the recognized events.

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A Comparison of HMMs and Dynamic Bayesian Networks for Recognizing Office Activities

Cited by 59 publications

References 14 publications

HMM-based Activity Recognition with a Ceiling RGB-D Camera

HMM-based Activity Recognition with a Ceiling RGB-D Camera

Probabilistic Recognition of Complex Event

A Framework Dealing with Uncertainty for Complex Event Recognition

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