Hand gesture recognition using input-output hidden Markov models

Marcel, Sébastien; Bernier, Olivier; Viallet, J.E.; Collobert, D.

doi:10.1109/afgr.2000.840674

Cited by 83 publications

(46 citation statements)

References 8 publications

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“…As subset of activity recognition, datasets for gesture recognition include ASL (Australian Sign Language) [144], FGnet [146], [149], [147], Pointing'04 [148], Cambridge Gesture Database [150], and the ChaLearn Gesture Challenge dataset [151]. The ASL [144] dataset consists of a wide set of samples of Auslan (Australian Sign Language) signs: 27 examples of each of 95 Auslan signs were captured from a native signer using high-quality position trackers.…”

Section: Datasetsmentioning

confidence: 99%

Video-Based Human Behavior Understanding: A Survey

Borges

Conci

Cavallaro

2013

IEEE Trans. Circuits Syst. Video Technol.

185

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Abstract-Understanding human behaviors is a challenging problem in computer vision that has recently seen important advances. Human behavior understanding combines image and signal processing, feature extraction, machine learning and 3D geometry. Application scenarios range from surveillance to indexing and retrieval, from patient care to industrial safety and sports analysis. Given the broad set of techniques used in videobased behavior understanding and the fast progress in this area, in this paper we organize and survey the corresponding literature, define unambiguously key terms and discuss links among fundamental building blocks ranging from human detection to action and interaction recognition. The advantages and the drawbacks of the methods are critically discussed, providing a comprehensive coverage of key aspects of video-based human behavior understanding, available datasets for experimentation and comparisons, and important open research issues.

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Section: Datasetsmentioning

confidence: 99%

Video-Based Human Behavior Understanding: A Survey

Borges

Conci

Cavallaro

2013

IEEE Trans. Circuits Syst. Video Technol.

185

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“…The traditional approaches focused on using www.ijacsa.thesai.org RGB data. Sebastiean Marcel [19] proposed the approach based on Input-output Hidden Markov Models [23]. Moreover, the state of the art local features are also used by Chieh-Chih Wang et al [4] and Y. Yao al.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Network and Polar Transformation Features for Static Hand Gesture Recognition in Depth Data

Viet¹,

Son²,

Ngoc³

2016

ijacsa

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Abstract-Static hand gesture recognition is an interesting and challenging problem in computer vision. It is considered a significant component of Human Computer Interaction and it has attracted many research efforts from the computer vision community in recent decades for its high potential applications, such as game interaction and sign language recognition. With the recent advent of the cost-effective Kinect, depth cameras have received a great deal of attention from researchers. It promoted interest within the vision and robotics community for its broad applications. In this paper, we propose the effective hand segmentation from the full depth image that is important step before extracting the features to represent for hand gesture. We also represent the novel hand descriptor explicitly encodes the shape and appearance information from depth maps that are significant characteristics for static hand gestures. We propose hand descriptor based on Polar Transformation coordinate is called Histogram of Polar Transformation (HPT) in order to capture both shape and appearance. Beside a robust hand descriptor, a robust classification model also plays a very important role in the hand recognition model. In order to have a high performance in recognition rate, we propose hybrid model for classification based on Sparse Auto-encoder and Deep Neural Network. We demonstrate large improvements over the state-ofthe-art methods on two challenging benchmark datasets are NTU Hand Digits and ASL Finger Spelling and achieve the overall accuracy as 97.7% and 84.58%, respectively. Our experiments show that the proposed method significantly outperforms stateof-the-art techniques.

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“…More recently, Marcel et al [4] have proposed Input/Output Hidden Markov Models (IOHMMs). An IOHMM is based on a non-homogeneous Markov chain where emission and transition probabilities depend on the input.…”

Section: Related Workmentioning

confidence: 99%

HMM and IOHMM for the Recognition of Mono- and Bi-Manual 3D Hand Gestures

Just¹,

Bernier²,

Marcel³

2004

Procedings of the British Machine Vision Conference 2004

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In this paper, we address the problem of the recognition of isolated complex mono-and bi-manual hand gestures. In the proposed system, hand gestures are represented by the 3D trajectories of blobs obtained by tracking colored body parts. In this paper, we study the results obtained on a complex database of mono-and bi-manual gestures. These results are obtained by using Input/Output Hidden Markov Model (IOHMM), implemented within the framework of an open source machine learning library, and are compared to Hidden Markov Model (HMM).

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Hand gesture recognition using input-output hidden Markov models

Cited by 83 publications

References 8 publications

Video-Based Human Behavior Understanding: A Survey

Video-Based Human Behavior Understanding: A Survey

Hybrid Deep Network and Polar Transformation Features for Static Hand Gesture Recognition in Depth Data

HMM and IOHMM for the Recognition of Mono- and Bi-Manual 3D Hand Gestures

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