• Title, Summary, Keyword: hand gesture recognition

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Hand Gesture Recognition using DP Matching from USB Camera Video (USB 카메라 영상에서 DP 매칭을 이용한 사용자의 손 동작 인식)

  • Ha, Jin-Young;Byeon, Min-Woo;Kim, Jin-Sik
    • Journal of Industrial Technology
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    • v.29 no.A
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    • pp.47-54
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    • 2009
  • In this paper, we proposed hand detection and hand gesture recognition from USB camera video. Firstly, we extract hand region extraction using skin color information from a difference images. Background image is initially stored and extracted from the input images in order to reduce problems from complex backgrounds. After that, 16-directional chain code sequence is computed from the tracking of hand motion. These chain code sequences are compared with pre-trained models using DP matching. Our hand gesture recognition system can be used to control PowerPoint slides or applied to multimedia education systems. We got 92% hand region extraction accuracy and 82.5% gesture recognition accuracy, respectively.

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HOG-HOD Algorithm for Recognition of Multi-cultural Hand Gestures (다문화 손동작 인식을 위한 HOG-HOD 알고리즘)

  • Kim, Jiye;Park, Jong-Il
    • Journal of Korea Multimedia Society
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    • v.20 no.8
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    • pp.1187-1199
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    • 2017
  • In recent years, research about Natural User Interface (NUI) has become focused because NUI system can give natural feelings for users in virtual reality. Most important thing in NUI system is how to communicate with the computer system. There are many things to interact with users such as speech, hand gestures, body actions. Among them, hand gesture is suitable for the purpose of NUI because people often use a relatively high frequency in daily life and hand gesture have meaning only by itself. This hand gestures called multi-cultural hand gesture and we proposed the method to recognize this kind of hand gestures. Proposed method is composed of Histogram of Oriented Gradients (HOG) used for hand shape recognition and Histogram of Oriented Displacements (HOD) used for hand center point trajectory recognition.

Hand Gesture Recognition Algorithm using Mathematical Morphology

  • Park, Jong-Ho;Ko, Duck-Young
    • Proceedings of the IEEK Conference
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    • pp.995-998
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    • 2002
  • In this paper, we present a fast algorithm for hand gesture recognition of a human from an image by using the directivity information of the fingers. To implement a fast recognition system, we applied the morphological shape decomposition. A proposed gesture recognition algorithm has been tested on the 300 ${\times}$ 256 digital images. Our experiments using image acquired image camera have shown that the proposed hand gesture recognition algorithm is effective.

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Real-Time Hand Gesture Recognition Based on Deep Learning (딥러닝 기반 실시간 손 제스처 인식)

  • Kim, Gyu-Min;Baek, Joong-Hwan
    • Journal of Korea Multimedia Society
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    • v.22 no.4
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    • pp.424-431
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    • 2019
  • In this paper, we propose a real-time hand gesture recognition algorithm to eliminate the inconvenience of using hand controllers in VR applications. The user's 3D hand coordinate information is detected by leap motion sensor and then the coordinates are generated into two dimensional image. We classify hand gestures in real-time by learning the imaged 3D hand coordinate information through SSD(Single Shot multibox Detector) model which is one of CNN(Convolutional Neural Networks) models. We propose to use all 3 channels rather than only one channel. A sliding window technique is also proposed to recognize the gesture in real time when the user actually makes a gesture. An experiment was conducted to measure the recognition rate and learning performance of the proposed model. Our proposed model showed 99.88% recognition accuracy and showed higher usability than the existing algorithm.

Hand Gesture Recognition for Understanding Conducting Action (지휘행동 이해를 위한 손동작 인식)

  • Je, Hong-Mo;Kim, Ji-Man;Kim, Dai-Jin
    • Proceedings of the Korean Information Science Society Conference
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    • pp.263-266
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    • 2007
  • We introduce a vision-based hand gesture recognition fer understanding musical time and patterns without extra special devices. We suggest a simple and reliable vision-based hand gesture recognition having two features First, the motion-direction code is proposed, which is a quantized code for motion directions. Second, the conducting feature point (CFP) where the point of sudden motion changes is also proposed. The proposed hand gesture recognition system extracts the human hand region by segmenting the depth information generated by stereo matching of image sequences. And then, it follows the motion of the center of the gravity(COG) of the extracted hand region and generates the gesture features such as CFP and the direction-code finally, we obtain the current timing pattern of beat and tempo of the playing music. The experimental results on the test data set show that the musical time pattern and tempo recognition rate is over 86.42% for the motion histogram matching, and 79.75% fer the CFP tracking only.

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Hand Gesture Recognition Suitable for Wearable Devices using Flexible Epidermal Tactile Sensor Array

  • Byun, Sung-Woo;Lee, Seok-Pil
    • Journal of Electrical Engineering and Technology
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    • v.13 no.4
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    • pp.1732-1739
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    • 2018
  • With the explosion of digital devices, interaction technologies between human and devices are required more than ever. Especially, hand gesture recognition is advantageous in that it can be easily used. It is divided into the two groups: the contact sensor and the non-contact sensor. Compared with non-contact gesture recognition, the advantage of contact gesture recognition is that it is able to classify gestures that disappear from the sensor's sight. Also, since there is direct contacted with the user, relatively accurate information can be acquired. Electromyography (EMG) and force-sensitive resistors (FSRs) are the typical methods used for contact gesture recognition based on muscle activities. The sensors, however, are generally too sensitive to environmental disturbances such as electrical noises, electromagnetic signals and so on. In this paper, we propose a novel contact gesture recognition method based on Flexible Epidermal Tactile Sensor Array (FETSA) that is used to measure electrical signals according to movements of the wrist. To recognize gestures using FETSA, we extracted feature sets, and the gestures were subsequently classified using the support vector machine. The performance of the proposed gesture recognition method is very promising in comparison with two previous non-contact and contact gesture recognition studies.

Controlling Slides using Hand tracking and Gesture Recognition (손의 추적과 제스쳐 인식에 의한 슬라이드 제어)

  • Fayyaz, Rabia;Rhee, Eun Joo
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.436-439
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    • 2012
  • The work is to the control the desktop Computers based on hand gesture recognition. This paper is worked en real time tracking and recognizes the hand gesture for controlling the slides based on hand direction such as right and left using a real time camera.

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A study on hand gesture recognition using 3D hand feature (3차원 손 특징을 이용한 손 동작 인식에 관한 연구)

  • Bae Cheol-Soo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.4
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    • pp.674-679
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    • 2006
  • In this paper a gesture recognition system using 3D feature data is described. The system relies on a novel 3D sensor that generates a dense range mage of the scene. The main novelty of the proposed system, with respect to other 3D gesture recognition techniques, is the capability for robust recognition of complex hand postures such as those encountered in sign language alphabets. This is achieved by explicitly employing 3D hand features. Moreover, the proposed approach does not rely on colour information, and guarantees robust segmentation of the hand under various illumination conditions, and content of the scene. Several novel 3D image analysis algorithms are presented covering the complete processing chain: 3D image acquisition, arm segmentation, hand -forearm segmentation, hand pose estimation, 3D feature extraction, and gesture classification. The proposed system is tested in an application scenario involving the recognition of sign-language postures.

A Memory-efficient Hand Segmentation Architecture for Hand Gesture Recognition in Low-power Mobile Devices

  • Choi, Sungpill;Park, Seongwook;Yoo, Hoi-Jun
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.17 no.3
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    • pp.473-482
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    • 2017
  • Hand gesture recognition is regarded as new Human Computer Interaction (HCI) technologies for the next generation of mobile devices. Previous hand gesture implementation requires a large memory and computation power for hand segmentation, which fails to give real-time interaction with mobile devices to users. Therefore, in this paper, we presents a low latency and memory-efficient hand segmentation architecture for natural hand gesture recognition. To obtain both high memory-efficiency and low latency, we propose a streaming hand contour tracing unit and a fast contour filling unit. As a result, it achieves 7.14 ms latency with only 34.8 KB on-chip memory, which are 1.65 times less latency and 1.68 times less on-chip memory, respectively, compare to the best-in-class.

Dynamic Hand Gesture Recognition using Guide Lines (가이드라인을 이용한 동적 손동작 인식)

  • Kim, Kun-Woo;Lee, Won-Joo;Jeon, Chang-Ho
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.47 no.5
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    • pp.1-9
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    • 2010
  • Generally, dynamic hand gesture recognition is formed through preprocessing step, hand tracking step and hand shape detection step. In this paper, we present advanced dynamic hand gesture recognizing method that improves performance in preprocessing step and hand shape detection step. In preprocessing step, we remove noise fast by using dynamic table and detect skin color exactly on complex background for controling skin color range in skin color detection method using YCbCr color space. Especially, we increase recognizing speed in hand shape detection step through detecting Start Image and Stop Image, that are elements of dynamic hand gesture recognizing, using Guideline. Guideline is edge of input hand image and hand shape for comparing. We perform various experiments with nine web-cam video clips that are separated to complex background and simple background for dynamic hand gesture recognition method in the paper. The result of experiment shows similar recognition ratio but high recognition speed, low cpu usage, low memory usage than recognition method using learning exercise.