Two Hand Gesture Based 3D Navigation in Virtual Environments

Rehman, Inam Ur; Ullah, Sadiq; Raees, Muhammad

doi:10.9781/ijimai.2018.07.001

Cited by 8 publications

(5 citation statements)

References 80 publications

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“…There are many works on gesture recognition for different purposes, some focusing on the whole body [6] while others focusing on a specific part as eyes [7] or hands [8]. Despite of the vast number of research works that have been published, there have been several limitations.…”

Section: Introductionmentioning

confidence: 99%

Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks

Khari¹,

Garg²,

Crespo³

et al. 2019

IJIMAI

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In this era, the interaction between Human and Computers has always been a fascinating field. With the rapid development in the field of Computer Vision, gesture based recognition systems have always been an interesting and diverse topic. Though recognizing human gestures in the form of sign language is a very complex and challenging task. Recently various traditional methods were used for performing sign language recognition but achieving high accuracy is still a challenging task. This paper proposes a RGB and RGB-D static gesture recognition method by using a fine-tuned VGG19 model. The fine-tuned VGG19 model uses a feature concatenate layer of RGB and RGB-D images for increasing the accuracy of the neural network. Finally, on an American Sign Language (ASL) Recognition dataset, the authors implemented the proposed model. The authors achieved 94.8% recognition rate and compared the model with other CNN and traditional algorithms on the same dataset.

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Section: Introductionmentioning

confidence: 99%

Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks

Khari¹,

Garg²,

Crespo³

et al. 2019

IJIMAI

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“…Rehman et al proposed novel two hand gestures for three-dimensional navigation of a car in a VR environment [9]. This system recognizes the relative positions of the thumbs of both hands by acquiring the color marker attached to the user's thumb from the camera attached to the HMD.…”

Section: Natural Gestures As System Inputmentioning

confidence: 99%

Extensible Neck: A Gesture Input Method to Extend/Contract Neck Virtually in Video See-through AR Environment

Yamazaki¹,

Ohnishi²,

Terada³

et al. 2022

Sensors

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With the popularization of head-mounted displays (HMDs), many systems for human augmentation have been developed. This will increase the opportunities to use such systems in daily life. Therefore, the user interfaces for these systems must be designed to be intuitive and highly responsive. This paper proposes an intuitive input method that uses natural gestures as input cues for systems for human augmentation. We investigated the appropriate gestures for a system that expands the movements of the user’s viewpoint by extending and contracting the neck in a video see-through AR environment. We conducted an experiment to investigate natural gestures by observing the motions when a person wants to extend his/her neck. Furthermore, we determined the operation method for extending/contracting the neck and holding the position through additional experiments. Based on this investigation, we implemented a prototype of the proposed system in a VR environment. Note that we employed a VR environment since we could test our method in various situations, although our target environment is AR. We compared the operability of the proposed method and the handheld controller using our prototype. The results confirmed that the participants felt more immersed using our method, although the positioning speed using controller input was faster than that of our method.

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“…These gestures were complex and hard to learn. Rehman et al [58] also used colored fingertips-based gestures for navigation in VE. The relative distance between two fingertips was used for controlling the navigation speed of the virtual object.…”

Section: Related Workmentioning

confidence: 99%

Fingertip Gestures Recognition Using Leap Motion and Camera for Interaction with Virtual Environment

et al. 2020

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The emergence in computing and the latest hardware technologies realized the use of natural interaction with computers. Gesture-based interaction is one of the prominent fields of natural interactions. The recognition and application of hand gestures in virtual environments (VEs) need extensive calculations due to the complexities involved, which directly affect the performance and realism of interaction. In this paper, we propose a new interaction technique that uses single fingertip-based gestures for interaction with VEs. The objective of the study is to minimize the computational cost, increase performance, and improve usability. The interaction involves navigation, selection, translation, and release of objects. For this purpose, we propose a low-cost camera-based system that uses a colored fingertip for the fastest and accurate recognition of gestures. We also implemented the proposed interaction technique using the Leap Motion controller. We present a comparative analysis of the proposed system with the Leap Motion controller for gesture recognition and operation. A VE was developed for experimental purposes. Moreover, we conducted a comprehensive analysis of two different recognition setups including video camera and the Leap Motion sensor. The key parameters for analysis were task accuracy, interaction volume, update rate, and spatial distortion of accuracy. We used the Standard Usability Scale (SUS) for system usability analysis. The experiments revealed that camera implementation was found with good performance, less spatial distortion of accuracy, and large interaction volume as compared to the Leap Motion sensor. We also found the proposed interaction technique highly usable in terms of user satisfaction, user-friendliness, learning, and consistency.

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Two Hand Gesture Based 3D Navigation in Virtual Environments

Cited by 8 publications

References 80 publications

Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks

Gesture Recognition of RGB and RGB-D Static Images Using Convolutional Neural Networks

Extensible Neck: A Gesture Input Method to Extend/Contract Neck Virtually in Video See-through AR Environment

Fingertip Gestures Recognition Using Leap Motion and Camera for Interaction with Virtual Environment

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