CVOct 18, 2019
Development of a hand pose recognition system on an embedded computer using CNNs
arXiv:1910.11100v1
Originality Synthesis-oriented
AI Analysis
This addresses the need for efficient human-machine interfaces on embedded devices, but it is incremental as it applies existing methods to a specific platform.
The paper tackled hand pose recognition for embedded computers by using hand tracking and CNNs, achieving 94.50% accuracy with fast response and low power consumption.
Demand of hand pose recognition systems are growing in the last years in technologies like human-machine interfaces. This work suggests an approach for hand pose recognition in embedded computers using hand tracking and CNNs. Results show a fast time response with an accuracy of 94.50% and low power consumption.