Human-vehicle interaction by hand sign understanding

Guo Dong; Xie Ming; Xiaoming Yin

doi:10.1117/12.354427

19 July 1999 Human-vehicle interaction by hand sign understanding

Guo Dong, Xie Ming, Xiaoming Yin

Proceedings Volume 3691, Enhanced and Synthetic Vision 1999; (1999) https://doi.org/10.1117/12.354427
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

The interactive ability of intelligent electric vehicle with human has the capital importance of convincing public to accept the existence and usage of intelligent electric vehicle, it can greatly enhance the safety of intelligent electric vehicle in public service. In this paper, an interactive model based on hand gesture understanding is represented, it offers more compact and intuitive meanings than other interactive models in an outdoor environment. The Typical hand gestures are defined to guide the motion of vehicle by considering gesture differentiation and human tendency in the model, they are classified as motion-oriented and direction-oriented gestures for different interactive intentions. The color distribution of human skin is analyzed in different color spaces, it reveals that human skin colors cluster in a specific region with the irregular appearance, they have more differences in intensity than colors among the people. A color model of human skin is built for hand gesture segmentation by using the training procedure of RCE neural network, it has the ability of delineating the pattern class with arbitrary appearance in feature space. The quality of hand gesture segmentation is further improved by the procedure of hand-forearm separation. A hand tracking mechanism is proposed to locate the hand by camera pan-tilt and zooming. The gesture recognition is implemented by template matching of multiple features.

Citation Download Citation

Guo Dong, Xie Ming, and Xiaoming Yin "Human-vehicle interaction by hand sign understanding", Proc. SPIE 3691, Enhanced and Synthetic Vision 1999, (19 July 1999); https://doi.org/10.1117/12.354427

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KEYWORDS

Image segmentation

Skin

Gesture recognition

Cameras

Light sources and illumination

Motion models

Prototyping

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