Action recognition using machine learning techniques for robots
Recent advancement in the processing capabilities of mobile chips has opened up the possibility of developing domestic companion robots with small form factors. The most intuitive way to interact with such robot is through human action, especially hand gesture. In this project, a robust static an...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/72346 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Recent advancement in the processing capabilities of mobile chips has opened up the
possibility of developing domestic companion robots with small form factors. The most
intuitive way to interact with such robot is through human action, especially hand
gesture. In this project, a robust static and dynamic gesture detector is built to achieve
real-time performance on a mobile processor. The proposed framework features a novel
hand hypotheses generator based on color and edge, a hand detector using Convolutional
Neural Network (CNN), a static gesture recognizer based on skin contour analysis, and a
hypotheses-tracking system based on Kalman Filter for increased performance and
consistency. The resulting system is robust to viewpoint, ambient lighting, and rotation,
capable of producing accurate results in various real-life settings.
Index Terms: Hand Gesture Recognition, Hand Detection, Hand Tracking,
Convolutional Neural Network |
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