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Journal of System Simulation

Abstract

Abstract: Vision-based gesture recognition is a commonly used means of human-computer interaction in the fields of virtual reality and game simulation. In practical applications, rapid changes in gesture movements will lead to blurred imaging with traditional RGB cameras or depth cameras, which brings great challenges to gesture recognition. To solve the above problems, a dynamic visual data gesture recognition method based on a multi-dimensional projection spatiotemporal event frame (STEF) is proposed by a using dynamic vision sensor to capture high-speed gesture movement information. The spatiotemporal information is embedded in the data projection surface and fused to form a multidimensional projection STEF, which overcomes the limitation of the time-domain information loss of the existing event frame expression method of dynamic visual information and improves the feature expression ability of dynamic visual sensing data. On this basis, advanced spiking neural networks are used to classify STEFs to realize gesture recognition. The recognition accuracy of the above method on the public dataset reaches 96.67%, which is better than similar methods, indicating that the proposed method can significantly improve the accuracy of gesture recognition in dynamic visual sensing data.

First Page

649

Last Page

658

CLC

TP391

Recommended Citation

Kang Lai, Zhang Yakun. Gesture Recognition for Dynamic Vision Sensor Based on Multi-dimensional Projection Spatiotemporal Event Frame[J]. Journal of System Simulation, 2024, 36(3): 649-658.

DOI

10.16182/j.issn1004731x.joss.23-0223

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