A ToF 3D database for hand gesture recognition

Simion, Georgiana; Căleanu, Cătălin Daniel

doi:10.1109/isetc.2012.6408145

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…The 3D dataset was produced by our research team at Politehnica University Timisoara and used a camera system based on the ToF principle [27]. In such a system, a modulated optical signal illuminates the scene and a photonic mixer device (PMD) sensor detects the reflected light.…”

Section: The Datasetmentioning

confidence: 99%

A PointNet-Based Solution for 3D Hand Gesture Recognition

Mirsu

Simion

Căleanu

et al. 2020

Sensors

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Gesture recognition is an intensively researched area for several reasons. One of the most important reasons is because of this technology’s numerous application in various domains (e.g., robotics, games, medicine, automotive, etc.) Additionally, the introduction of three-dimensional (3D) image acquisition techniques (e.g., stereovision, projected-light, time-of-flight, etc.) overcomes the limitations of traditional two-dimensional (2D) approaches. Combined with the larger availability of 3D sensors (e.g., Microsoft Kinect, Intel RealSense, photonic mixer device (PMD), CamCube, etc.), recent interest in this domain has sparked. Moreover, in many computer vision tasks, the traditional statistic top approaches were outperformed by deep neural network-based solutions. In view of these considerations, we proposed a deep neural network solution by employing PointNet architecture for the problem of hand gesture recognition using depth data produced by a time of flight (ToF) sensor. We created a custom hand gesture dataset, then proposed a multistage hand segmentation by designing filtering, clustering, and finding the hand in the volume of interest and hand-forearm segmentation. For comparison purpose, two equivalent datasets were tested: a 3D point cloud dataset and a 2D image dataset, both obtained from the same stream. Besides the advantages of the 3D technology, the accuracy of the 3D method using PointNet is proven to outperform the 2D method in all circumstances, even the 2D method that employs a deep neural network.

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Section: The Datasetmentioning

confidence: 99%

A PointNet-Based Solution for 3D Hand Gesture Recognition

Mirsu

Simion

Căleanu

et al. 2020

Sensors

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“…Current release, described in details in our previous work [52], contain multiple subjects expressing six static hand poses and four dynamic hand gestures, as exemplified in Fig. (4.20).…”

Section: D Data Acquisitionmentioning

confidence: 99%

Compositional and Hierarchical Semantic Frameworks for Hand Gesture Recognition

Simion¹,

David²,

Căleanu³

et al. 2015

Gate to Computer Science and Research

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This chapter presents some new approaches in hand gesture recognition from monocular and from 3D images. After introducing the main trends from literature, the chapter addresses the hand gesture recognition problem in a compositional framework. The ability of compositional methods to capture and extract semantic information from selected salient parts of the images is demonstrated for the hand gesture application. Performances on images with static hand gestures executed on uniform backgrounds rival the state of the art, while living a lot of room for further optimization and improvement. The second work reported in this chapter is focused on using multiple cues to overcome difficult problems arising when dynamic gestures are executed in front of heterogeneous backgrounds, with camouflage and sudden illumination changes. Ideas from robust estimation are integrated in the proposed approach. Finally, some preliminary results of hand gesture recognition obtained with images generated by 3D time of flight image sensors, presumed to become prevalent in the near future, are presented.

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“…Previously, studies related to hand gesture recognition were vision-based, usually using RGB cameras [3,4] or time-of-flight cameras [5][6][7], which have a high accuracy in recognizing whether an object is a hand or not. However, the hand gesture images captured from cameras usually contain personal or environmental information, which might cause privacy issues [8].…”

Section: Introductionmentioning

confidence: 99%

Implementing a Hand Gesture Recognition System Based on Range-Doppler Map

Jhaung

Lin

Zha

et al. 2022

Sensors

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There have been several studies of hand gesture recognition for human–machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range, velocity, and angle. With these signatures, we can customize our system to different scenarios. We proposed an end-to-end training deep learning model (neural network and long short-term memory), that extracts the transformed radar signals into features and classifies the extracted features into hand gesture labels. In our training data collecting effort, a camera is used only to support labeling hand gesture data. The accuracy of our model can reach 98%.

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A ToF 3D database for hand gesture recognition

Cited by 8 publications

References 14 publications

A PointNet-Based Solution for 3D Hand Gesture Recognition

A PointNet-Based Solution for 3D Hand Gesture Recognition

Compositional and Hierarchical Semantic Frameworks for Hand Gesture Recognition

Implementing a Hand Gesture Recognition System Based on Range-Doppler Map

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