A windowed dynamic time warping approach for 3D continuous hand gesture recognition

Cheng, Hong; Luo, Jun; Chen, Xuewen

doi:10.1109/icme.2014.6890302

Cited by 22 publications

(13 citation statements)

References 14 publications

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“…The gestures are mainly represented by the two-hand movements. There are some other action/activity datasets such as MSR Action 3D, MSR Daily Activity Dataset [90], UESTC-DHG [49] and LIRIS human activity dataset. More or less, they all provide several easy hand trajectory gestures in the RGB-D domain.…”

Section: B Trajectory Gesture Datasetsmentioning

confidence: 99%

“…ASL and ChaLearn datasets). Recently, Cheng et al proposed a Windowed Dynamic Time Warping (WDTW) approaches [49] for 3D continuous hand trajectory gesture recognition. In their work, a parameterized searching window was introduced in the cost matrix of traditional DTW approaches to detect the beginning and end of the specific gesture from an infinite trajectory gesture sequence.…”

Section: Continuous Hand Gesture Recognitionmentioning

confidence: 99%

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Survey on 3D Hand Gesture Recognition

Cheng

Yang

Liu

2016

IEEE Trans. Circuits Syst. Video Technol.

Self Cite

290

110

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3D hand gesture recognition has attracted increasing research interests in computer vision, pattern recognition and human-computer interaction. The emerging depth sensors greatly inspired various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors. We first review the commercial depth sensors and public datasets which are widely used in this field. Then, we review the state-of-the-art research for 3D hand gesture recognition in four aspects: 3D hand modeling, static hand gesture recognition, hand trajectory gesture recognition and continuous hand gesture recognition. While the emphasis is on 3D hand gesture recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.

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Section: B Trajectory Gesture Datasetsmentioning

confidence: 99%

Section: Continuous Hand Gesture Recognitionmentioning

confidence: 99%

Survey on 3D Hand Gesture Recognition

Cheng

Yang

Liu

2016

IEEE Trans. Circuits Syst. Video Technol.

Self Cite

290

110

View full text Add to dashboard Cite

show abstract

“…The Dynamic Time Warping (DTW) [20] is a method for finding an optimal alignment between two given time-series which may vary in speed and time. DTW-based algorithms are currently used for speech recognition [21], gesture recognition [22], robot learning [23], gait analysis [24] and for other sensorbased applications. The fundamental functionality of DTW is to define an optimal warping path (alignment) and to calculate the DTW distance (similarity) between two given time-series.…”

Section:  Automatic Selection Of Similar Demonstrationsmentioning

confidence: 99%

Robot Learning of Object Manipulation Task Actions From Human Demonstrations

Kyrarini

Haseeb²,

Ristić-Durrant³

et al. 2017

FU Mech Eng

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Abstract. Robot learning from demonstration is a method which enables robots to learn in a similar way as humans. In this paper, a framework that enables robots to learn from multiple human demonstrations via kinesthetic teaching is presented. The subject of learning is a high-level sequence of actions, as well as the low-level trajectories necessary to be followed by the robot to perform the object manipulation task. The multiple human demonstrations are recorded and only the most similar demonstrations are selected for robot learning. The high-level learning module identifies the sequence of actions of the demonstrated task. Using Dynamic Time Warping (DTW) and Gaussian Mixture Model (GMM), the model of demonstrated trajectories is learned.The learned trajectory is generated by Gaussian mixture regression (GMR) from the learned Gaussian mixture model. In online working phase, the sequence of actions is identified and experimental results show that the robot performs the learned task successfully.

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“…One phase of signal interpretation is the motion stage division, which is normally performed physically by experts and is a relentless assignment [16]. Since various people have distinctive hands and fingers in physical measurement, a similar type of a motion made by two distinct people may create nonidentical numerical information when the motions are measured by normal detecting gadgets [17,18]. Normally, some type of changes might be required for highlight acknowledgment.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Framework for Sign Gesture Recognition using Hidden Markov Model and Adaptive Histogram Technique

Kaluri¹,

Reddy²

2017

IJIES

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Gesture based communication is the fundamental method for correspondence for those with hearing and vocal incapacities. Communication via gestures comprises of making shapes or developments with human hands as for the head or other body parts. In this paper, we propose a new framework for recognizing sign gestures by using Hidden Markov Model (HMM) and Histogram based methods. Initially, the noise of an image will be eliminated by Wiener Filter and the image will be segmented with the help of Histogram oriented methods -Adaptive Histogram technique and then features will be extracted. The extracted features will be given to the HMM for training and recognition of gestures. Our experimental results show a better performance in terms of recognizing gestures from a blurred image compared to the existing segmentation methods.

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A windowed dynamic time warping approach for 3D continuous hand gesture recognition

Cited by 22 publications

References 14 publications

Survey on 3D Hand Gesture Recognition

Survey on 3D Hand Gesture Recognition

Robot Learning of Object Manipulation Task Actions From Human Demonstrations

An Enhanced Framework for Sign Gesture Recognition using Hidden Markov Model and Adaptive Histogram Technique

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