Pointing Gesture Interface for Large Display Environments Based on the Kinect Skeleton Model

Kim, Hansol; Kim, Yoonkyung; Ko, Daejune; Kim, Jin Man; Lee, Eui Chul

doi:10.1007/978-3-642-55038-6_80

“… Lab-made gesture recognition software based on the upper body skeleton model in the Kinect SDK. (a) Pointing gesture recognition software (white dot: shoulder point, yellow dot: hand point) [ 10 ]. (b) Finger counting gesture recognition software.…”

Section: Figurementioning

confidence: 99%

Method for User Interface of Large Displays Using Arm Pointing and Finger Counting Gesture Recognition

Kim

¹

,

Kim

²

,

Lee

³

2014

The Scientific World Journal

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Although many three-dimensional pointing gesture recognition methods have been proposed, the problem of self-occlusion has not been considered. Furthermore, because almost all pointing gesture recognition methods use a wide-angle camera, additional sensors or cameras are required to concurrently perform finger gesture recognition. In this paper, we propose a method for performing both pointing gesture and finger gesture recognition for large display environments, using a single Kinect device and a skeleton tracking model. By considering self-occlusion, a compensation technique can be performed on the user's detected shoulder position when a hand occludes the shoulder. In addition, we propose a technique to facilitate finger counting gesture recognition, based on the depth image of the hand position. In this technique, the depth image is extracted from the end of the pointing vector. By using exception handling for self-occlusions, experimental results indicate that the pointing accuracy of a specific reference position was significantly improved. The average root mean square error was approximately 13 pixels for a 1920 × 1080 pixels screen resolution. Moreover, the finger counting gesture recognition accuracy was 98.3%.

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“…In practice, many researchers have found difficulties in achieving real-time, accurate results of a pointing finger using their gesture recognition system. Kim [104] [105] describes a 'pointing gesture recognition method' that fails because it only uses basic static gestures, always treating one index finger extended up in air as a pointing gesture. This conclusion is premature and not representative of how pointing is done in real life: in this dissertation, the researcher has determined that people do not commonly point with their limb in tandem with their finger -the 'pointing' gesture is often performed with just the finger, and does not require synchronization with the entire arm, which Kim has incorrectly assumed.…”

Section: Introductionmentioning

confidence: 99%

Machine recognition of human gestures through principal joint variable analysis

Bulzacki¹

2021

Preprint

0

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Gestures are a fundamental part of human communication and are becoming a key component of human-computer interaction. Traditionally, to teach computers to recognize specific gestures, researchers have used a sensor, usually a camera, to collect large gesture datasets, which are then classified and structured using machine learning techniques. Yet finding a way to confidently differentiate between several gesture classes has proven to be rather difficult for those working in the gesture recognition field. To capture the samples of movements necessary to train gesture recognition systems, the first step is to provide research participants with appropriate instructions. As collecting gesture data is the crucial first step of creating a robust gesture dataset, this dissertation will examine the modalities of instruction used in gesture recognition research to examine whether appropriate directives are conveyed to research participants. These experiments will result in the creation of a new dataset, the PJVA-20 dataset, comprised of 50 samples of 20 gesture classes sampled from 6 participants. After collecting the gesture samples of the PJVA-20 dataset, this dissertation will establish the benchmark recognition system PJVA — chiefly comprised of AMFE, Polynomial Motion Approximation, and Principal Component Analysis (PCA)—to contribute to the gesture recognition literature in terms of novel gesture recognition algorithms that can achieve high speed and accuracy results. This also involves examining studies in the gesture recognition literature to determine which machine learning algorithms offer reliability, speed, and accuracy for solving complex gesture recognition problems, as well as experimenting and testing the PJVA approach against other researchers in the Computer Vision and Machine Learning fields. In particular, the MSRC-12 research provides a benchmark point of comparison for research in this field. To test the quality of samples on the PJVA-20 against the MSRC-12, a new method is established for extracting motion feature vectors through a novel gesture recognition approach, AMFE. This is tested by applying PJVA to extract and label gesture data from both the MSRC- 12 and PJVA-20 datasets.

show abstract

“…In practice, many researchers have found difficulties in achieving real-time, accurate results of a pointing finger using their gesture recognition system. Kim [104] [105] describes a 'pointing gesture recognition method' that fails because it only uses basic static gestures, always treating one index finger extended up in air as a pointing gesture. This conclusion is premature and not representative of how pointing is done in real life: in this dissertation, the researcher has determined that people do not commonly point with their limb in tandem with their finger -the 'pointing' gesture is often performed with just the finger, and does not require synchronization with the entire arm, which Kim has incorrectly assumed.…”

Section: Introductionmentioning

confidence: 99%

Machine recognition of human gestures through principal joint variable analysis

Bulzacki¹

2021

Preprint

0

View full text Add to dashboard Cite

Gestures are a fundamental part of human communication and are becoming a key component of human-computer interaction. Traditionally, to teach computers to recognize specific gestures, researchers have used a sensor, usually a camera, to collect large gesture datasets, which are then classified and structured using machine learning techniques. Yet finding a way to confidently differentiate between several gesture classes has proven to be rather difficult for those working in the gesture recognition field. To capture the samples of movements necessary to train gesture recognition systems, the first step is to provide research participants with appropriate instructions. As collecting gesture data is the crucial first step of creating a robust gesture dataset, this dissertation will examine the modalities of instruction used in gesture recognition research to examine whether appropriate directives are conveyed to research participants. These experiments will result in the creation of a new dataset, the PJVA-20 dataset, comprised of 50 samples of 20 gesture classes sampled from 6 participants. After collecting the gesture samples of the PJVA-20 dataset, this dissertation will establish the benchmark recognition system PJVA — chiefly comprised of AMFE, Polynomial Motion Approximation, and Principal Component Analysis (PCA)—to contribute to the gesture recognition literature in terms of novel gesture recognition algorithms that can achieve high speed and accuracy results. This also involves examining studies in the gesture recognition literature to determine which machine learning algorithms offer reliability, speed, and accuracy for solving complex gesture recognition problems, as well as experimenting and testing the PJVA approach against other researchers in the Computer Vision and Machine Learning fields. In particular, the MSRC-12 research provides a benchmark point of comparison for research in this field. To test the quality of samples on the PJVA-20 against the MSRC-12, a new method is established for extracting motion feature vectors through a novel gesture recognition approach, AMFE. This is tested by applying PJVA to extract and label gesture data from both the MSRC- 12 and PJVA-20 datasets.

show abstract

Pointing Gesture Interface for Large Display Environments Based on the Kinect Skeleton Model

Cited by 12 publications

References 4 publications

Method for User Interface of Large Displays Using Arm Pointing and Finger Counting Gesture Recognition

Method for User Interface of Large Displays Using Arm Pointing and Finger Counting Gesture Recognition

Machine recognition of human gestures through principal joint variable analysis

Machine recognition of human gestures through principal joint variable analysis

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