Understanding human interaction in RGB-D videos
In this report, a human hand gesture recognition system is proposed. The system can understand both static and dynamic human hand gestures. So far, the system is able to recognize 9 static hand gestures: numbers from one to nine; and 1 dynamic hand gesture: number ten. In the system impleme...
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| Main Author: | |
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/61366 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In this report, a human hand gesture recognition system is proposed. The system can
understand both static and dynamic human hand gestures. So far, the system is able to
recognize 9 static hand gestures: numbers from one to nine; and 1 dynamic hand gesture:
number ten. In the system implementation, a right-hand CyberGlove II is used to get the
accurate and stable hand joints information for the static hand gesture recognition. Based on
the results of static classification, together with the hand joint motion information from
Microsoft Kinect, dynamic hand gestures can be classified. In addition, and effective and fast
human hand gesture recognition algorithm is proposed to manage the data from sensors and
achieve classification results in real time. To verify the effectiveness of the system, a human
hand gesture sample dataset containing 250 samples collected from 5 people of difference
body sizes is constructed. The testing results show that the algorithm is able to understand
human hand gestures accurately and fast. |
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