A robust gesture recognition based on depth data

Jaemin, L.; Takimoto, Hironori; Yamauchi, Hitoshi; Kanazawa, Angjoo; Mitsukura, Yasue

doi:10.1109/fcv.2013.6485474

Cited by 18 publications

(11 citation statements)

References 3 publications

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“…Raptis et al [2011] present a classification system to recognize the dance actions in simplified skeletal space. Jaemin et al [2013] use the arm angle information and the simple hand gestures to classify the actions. Fujimura et al [2004] implement an algorithm to classify hand posture for more complex situations.…”

Section: Related Workmentioning

confidence: 99%

Somatosensory interaction for real-time large scale roaming

Jiang

Meng

Liu

et al. 2013

Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry

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Real-time somatosensory roaming in large-scale scene is a challenging problem because of great computation for the scene rendering, the real-time posture recognition and the instant response to the user's action. We present a novel somatosensory interactive roaming method in a realistic large-scale forest scene based on Microsoft Kinect. First, we construct a realistic large-scale forest scene based on GPU acceleration. Then for each action of the user, we encode the directions between joints into direction codes, and generate a posture vector which is used to compare with our posture template table to achieve posture recognition. Combining our forest scene with our multi-thread posture recognition, we can somatosensory interactively control roaming direction, velocity, and changes of light and shadow in real-time for the scene with millions of triangles. Our posture recognition algorithm is very fast and can response to the change of the user's posture in time. Experiments demonstrate that our method could successfully roam in the large-scale scene with a natural manner, which provides a feasible solution for the challenging problem.

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Section: Related Workmentioning

confidence: 99%

Somatosensory interaction for real-time large scale roaming

Jiang

Meng

Liu

et al. 2013

Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry

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“…The proposed method is compared with conventional method using 14 Japanese sign languages. 4 subjects were involved whereby each of the subjects was performed 3 times per ach sign language [18].…”

Section: Experiments Protocolmentioning

confidence: 99%

The applications of Microsoft Kinect for human motion capture and analysis : A review

Ismail

Basah

2015

2015 2nd International Conference on Biomedical Engineering (ICoBE)

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Microsoft Kinect is one of the inventions used in various applications including human motion capture. In this paper, the processes involved for human motion capture and analysis using Microsoft Kinect is demonstrated through a review. This review includes the data acquisition protocol, data collection and data analysis based on the computer vision field. This paper is focused on rehabilitation and biomechanics application through human motion analysis.

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“…In paper [11] Lee Jaemin et al proposes a method for gesture recognition using depth data obtained by the Kinect sensor. The proposed Gesture Recognition System consists of four steps.…”

Section: Introductionmentioning

confidence: 99%

Gesture recognition using Kinect in a virtual classroom environment

Hariharan

Padmini

Gopalakrishnan

2014

2014 Fourth International Conference on Digital Information and Communication Technology and Its Applications (DICTAP)

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In an E-learning classroom, students and professors are at different geographic locations. In the present E-learning system, the professor and the student interact with each other through simple internet applications. The disadvantage in this type of classroom set-up is that students in a remote location will not receive the same attention as those ones in a real classroom. Students in a remote location will only be seen on a small screen and the clarity of the video might be poor. Furthermore, the professor in a real classroom might focus only on students who are interacting face-to-face with him. The professor may not know the details of the remote students and how well they understand his class lecture. To solve this problem, this paper describes a system, based on Kinect TM , to make the E-learning class lecture more interactive for the remote students. For example, when a student in a remote location, raises his/her hand to talk to a professor, the Kinect camera detects the student, identifies the gesture of the remote student, zooms onto the remote student, and displays details such as his/her name and background of study. Thus the professor can pay equal attention to both the remote and local students.

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A robust gesture recognition based on depth data

Cited by 18 publications

References 3 publications

Somatosensory interaction for real-time large scale roaming

Somatosensory interaction for real-time large scale roaming

The applications of Microsoft Kinect for human motion capture and analysis : A review

Gesture recognition using Kinect in a virtual classroom environment

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