A Real-Time Gesture Recognition System with FPGA Accelerated ZynqNet Classification

Núñez-Prieto, Ricardo; Gómez, Pablo Correa; Liu, Liang

doi:10.1109/norchip.2019.8906956

Cited by 5 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nunez-Prieto et al [ 25 ] presented a real-time gesture recognition system using a regular phone camera for hand-data acquisition using FPGA implementation as an accelerator. In their work, a CNN was used for classification.…”

Section: Related Workmentioning

confidence: 99%

Architecture Design and VLSI Implementation of 3D Hand Gesture Recognition System

Tsai

2021

Sensors

View full text Add to dashboard Cite

With advancements in technology, more and more research is being focused on enhancing daily life quality and convenience. Along with the increase in the development of gesture control systems, many controllers, such as the keyboard, mouse, and other devices, have been replaced with remote control products, which are gradually becoming more intuitive for users. However, vision-based hand gesture recognition systems still have many problems to overcome. Most hand detection methods adopt a skin filter or motion filter for pre-processing. However, in a noisy environment, it is not easy to correctly extract interesting objects. In this paper, a VLSI design with dual-cameras has been proposed to construct a depth map with a stereo matching algorithm and recognize hand gestures. The proposed system adopts an adaptive depth filter to separate interesting foreground objects from the background. We also propose dynamic gesture recognition using depth and coordinate information. The system can perform static and dynamic gesture recognition. The ASIC design is implemented in TSMC 90 nm with about 47.3 K gate counts, and 27.8 mW of power consumption. The average accuracy of each gesture recognition is 83.98%.

show abstract

Section: Related Workmentioning

confidence: 99%