Dynamic Hand Gesture Recognition With Leap Motion Controller

Lü, Wei; Zheng, Tong; Chu, Jinghui

doi:10.1109/lsp.2016.2590470

Cited by 213 publications

(112 citation statements)

References 16 publications

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“…In the current literature, hand and body gesture recognition are based on a conventional scheme: the features are acquired from one or more sensors (such as Kinect [30]- [32] and LMC [8], [33]) and machine learning techniques (e.g., Support Vector Machine (SVM) [34], [35], Hidden Markov Models [36], [37] or Convolutional Neural Networks (CNNs) [26], [38]) are used to perform a classification phase. A reference work is reported in [34], where SVM is used with Histogram of Oriented Gradients (HOG) as feature vectors.…”

Section: Related Workmentioning

confidence: 99%

“…The Hidden Conditional Random Field (HCRF) method proposed in Wang et al [36] is instead used to recognize different human gestures. Lu et al [8] use an extension of the HCRF to recognize dynamic hand gestures driven by depth data. Regarding the hand pose estimation, the solution proposed in Li et al [37] shows excellent results by applying a Randomized Decision Tree (RDT).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Recurrent Neural Networks and Leap Motion Controller for the Recognition of Sign Language and Semaphoric Hand Gestures

Avola

Bernardi

Cinque

et al. 2019

IEEE Trans. Multimedia

153

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In human interactions, hands are a powerful way of expressing information that, in some cases, can be used as a valid substitute for voice, as it happens in Sign Language. Hand gesture recognition has always been an interesting topic in the areas of computer vision and multimedia. These gestures can be represented as sets of feature vectors that change over time. Recurrent Neural Networks (RNNs) are suited to analyse this type of sets thanks to their ability to model the long term contextual information of temporal sequences. In this paper, a RNN is trained by using as features the angles formed by the finger bones of human hands. The selected features, acquired by a Leap Motion Controller (LMC) sensor, have been chosen because the majority of human gestures produce joint movements that generate truly characteristic corners. A challenging subset composed by a large number of gestures defined by the American Sign Language (ASL) is used to test the proposed solution and the effectiveness of the selected angles. Moreover, the proposed method has been compared to other state of the art works on the SHREC dataset, thus demonstrating its superiority in hand gesture recognition accuracy.• the search of a robust solution able to recognize also gestures that are similar to each other; • the achievement of the highest accuracy level compared with works of the current literature.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Exploiting Recurrent Neural Networks and Leap Motion Controller for the Recognition of Sign Language and Semaphoric Hand Gestures

Avola

Bernardi

Cinque

et al. 2019

IEEE Trans. Multimedia

153

View full text Add to dashboard Cite

show abstract

“…Developers then can assign each of the static gesture [5] variable to an item in a game and the dynamic gesture [9] variables to trigger action sequences, such as firing a gun or throwing an object. Here we introduce a concept of "gesture blending" -the smooth transition from a static object to an action sequence with that object.…”

Section: Methodsmentioning

confidence: 99%

Representation of Intractable Objects and Action Sequences in VR Using Hand Gesture Recognition

Savosin

Prakoonwit

Tian

et al. 2017

E-Learning and Games

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Abstract. We propose a novel approach on using static and dynamic gesture recognition in VR games to represent interactive objects in games, such as equipment system, weapons and handy-tools. We examine various applications of gesture recognition in games, learning, medicine and VR, including how developers currently use the bundles of HDM devices paired with hand tracking sensors. The proposed approach provides game developers with a control over recording gestures and binding them to in-game intractable objects and equipment.

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“…The Leap Motion's movement recognition has also been investigated [16][17][18][19][20][21]. Marin et al [16,17] conducted research on the multiclass classifier by coupling Leap Motion with a Kinect and depth camera, while Vikram et al [18] studied the recognition of handwritten characters.…”

Section: Related Workmentioning

confidence: 99%

“…Marin et al [16,17] conducted research on the multiclass classifier by coupling Leap Motion with a Kinect and depth camera, while Vikram et al [18] studied the recognition of handwritten characters. Lu et al [19] proposed the Hidden Conditional Neural Field (HCNF) classifier to recognize the moving gestures. Boyali et al [20] researched the robotic wheelchair control, which applied block sparse, sparse representative, and classification.…”

Section: Related Workmentioning

confidence: 99%

Structural Motion Grammar for Universal Use of Leap Motion: Amusement and Functional Contents Focused

Lee¹,

Lee²,

Chin³

2018

Journal of Sensors

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Motions using Leap Motion controller are not standardized while the use of it is spreading in media contents. Each content defines its own motions, thereby creating confusion for users. Therefore, to alleviate user inconvenience, this study categorized the commonly used motion by Amusement and Functional Contents and defined the Structural Motion Grammar that can be universally used based on the classification. To this end, the Motion Lexicon was defined, which is a fundamental motion vocabulary, and an algorithm that enables real-time recognition of Structural Motion Grammar was developed. Moreover, the proposed method was verified by user evaluation and quantitative comparison tests.

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Dynamic Hand Gesture Recognition With Leap Motion Controller

Cited by 213 publications

References 16 publications

Exploiting Recurrent Neural Networks and Leap Motion Controller for the Recognition of Sign Language and Semaphoric Hand Gestures

Exploiting Recurrent Neural Networks and Leap Motion Controller for the Recognition of Sign Language and Semaphoric Hand Gestures

Representation of Intractable Objects and Action Sequences in VR Using Hand Gesture Recognition

Structural Motion Grammar for Universal Use of Leap Motion: Amusement and Functional Contents Focused

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