Feature selection for visual gesture recognition using hidden Markov models

Montero, José Antonio; Sucar, Luis Enrique

doi:10.1109/enc.2004.1342606

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…The selection of accurate and general gesture features is one of the most pursued goals in gesture recognition [29,30,31,32,33,34]. In practice, features are selected according to the characteristics of the gestures, and the application domain.…”

Section: Gesture Featuresmentioning

confidence: 99%

A Comparison of Dynamic Naive Bayesian Classifiers and Hidden

Avilés-Arriaga¹,

Sucar-Succar²,

Mendoza-Durán³

et al. 2011

JART

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In this paper we present a study to assess the performance of dynamic naive Bayesian classifiers (DNBCs) versusstandard hidden Markov models (HMMs) for gesture recognition. DNBCs incorporate explicit conditional independence among gesture features given states into HMMs. We show that this factorization offers competitive classification rates and error dispersion, it requires fewer parameters and it improves training time considerably in the presence of several attributes. We propose a set of qualitative and natural set of posture and motion attributes to describe gestures. We show that these posture-motion features increase recognition rates significantly in comparison to motion features. Additionally, an adaptive skin detection approach to cope with multiple users and different lighting conditions is proposed. We performed one of the most extensive experimentation presented in the literature to date that considers gestures of a single user, multiple people and with variations on distance and rotation using a gesture database with 9441 examples of 9 different classes performed by 15 people. Results show the effectiveness of the overall approach and the reliability of DNBCs in gesture ecognition.

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Section: Gesture Featuresmentioning

confidence: 99%

A Comparison of Dynamic Naive Bayesian Classifiers and Hidden

Avilés-Arriaga¹,

Sucar-Succar²,

Mendoza-Durán³

et al. 2011

JART

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show abstract

“…A well-known algorithm that solves the problem in polynomial time is the Baum-Welch, a specialization of the EM algorithm. Detailed information on the Baum-Welch algorithm and HMMs in general can be found in [4,13,8,9].…”

Section: Hidden Markov Modelsmentioning

confidence: 99%

Hidden Markov Models Applied to Snakes Behavior Identification

Gonçalves

Silva

Machado

et al. 2007

Advances in Image and Video Technology

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This paper presents an application of the hidden Markov models (HMMs) to the recognition of snakes behaviors, an important and hard problem that, as far as the authors know, has not been tackled before, by the computer vision community. Experiments were conducted using different HMM configurations, including modifications on the number of internal states and the initialization procedures. The best results have shown a 84% correct classification rate, using HMMs with 4 states and an initialization procedure based on the K-Means algorithm.

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“…In contrast to previous approaches relying on monocular data (e.g., [7], [8], [9]), our system works under realistic settings such as varying and difficult lighting conditions, multiple people, and cluttered background. On a notebook computer, we achieve a frame rate of 20 fps and are able to spot gestures as well as to recognize them, i.e., our system distinguishes between previously learned gestures and irrelevant or unconscious movements.…”

Section: Introductionmentioning

confidence: 98%

“…To compute a 7-dimensional feature vector, they describe the region corresponding to the moving body parts using statistics such as image moments. Montero and Sucar [9] use a ceilingmounted camera and apply a back-projection using a given color histogram to locate a hand on a desk. Given features based on the 2D trajectory of the hand, the authors apply a HMM to recognize typical office movements such as writing, using the mouse, etc.…”

Section: Related Workmentioning

confidence: 99%

Recognizing complex, parameterized gestures from monocular image sequences

Axenbeck

Bennewitz

Behnke

et al. 2008

Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots

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Abstract-Robotic assistants designed to coexist and communicate with humans in the real world should be able to interact with them in an intuitive way. This requires that the robots are able to recognize typical gestures performed by humans such as head shaking/nodding, hand waving, or pointing. In this paper, we present a system that is able to spot and recognize complex, parameterized gestures from monocular image sequences. To represent people, we locate their faces and hands using trained classifiers and track them over time. We use few, expressive features extracted out of this compact representation as input to hidden Markov models (HMMs). First, we segment gestures into distinct phases and train HMMs for each phase separately. Then, we construct composed HMMs, which consist of the individual phase-HMMs. Once a specific phase is recognized, we estimate the parameter of the current gesture, e.g., the target of a pointing gesture. As we demonstrate in the experiments, our method is able to robustly locate and track hands, despite of the fact that they can take a large number of substantially different shapes. Based on this, our system is able to reliably spot and recognize a variety of complex, parameterized gestures.

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Feature selection for visual gesture recognition using hidden Markov models

Cited by 9 publications

References 12 publications

A Comparison of Dynamic Naive Bayesian Classifiers and Hidden

A Comparison of Dynamic Naive Bayesian Classifiers and Hidden

Hidden Markov Models Applied to Snakes Behavior Identification

Recognizing complex, parameterized gestures from monocular image sequences

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