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

Tsai, Tsung‐Han; Tsai, Yih-Ru

doi:10.3390/s21206724

Cited by 4 publications

(4 citation statements)

References 30 publications

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“…It can be seen from the table that the recognition accuracy is lower than that of simple background, and the average recognition accuracy is 90.83%, which is due to the interference of other colors close to the skin color in the complex background. By comparison, the recognition accuracy of the system is improved compared with other gesture recognition systems based on FPGA [9] and VLSI [10]. Compared with the gesture recognition using deep convolutional neural network in paper [11], although the recognition accuracy is 1.5% lower in the simple background, it is 5% higher in the complex background.…”

Section: Recognition Accuracy Analysismentioning

confidence: 92%

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Research and implementation of dynamic gesture recognition system based on ZYNQ

Liu

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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At present, gesture has become an important channel of human-computer interaction, and gesture recognition has been widely used in various fields. In this paper, the dynamic gesture recognition technology is studied from algorithm and system implementation for portable devices which require high real-time performance. The algorithm mainly uses the region of interest extraction based on face recognition, skin color detection based on HCrCg color space and gesture motion track marking based on scanline seed filling algorithm. The system is implemented by Xilinx ZYNQ, and a SOPC system architecture based on ARM Cortex-A9 hard core and ARM Cortex-M3 soft core and FPGA is proposed. The scanline seed filling algorithm with long running time is designed as a hardware accelerator to improve the running speed. Through the test of the prototype, the recognition accuracy can reach 95.75% in a simple background and 90.83% in a complex background. The average running time of the system is only 0.68 seconds, which is more than 30% faster than using pure software method. The system has good performance in recognition accuracy and running speed.

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Section: Recognition Accuracy Analysismentioning

confidence: 92%

“…Paper [9] proposed a retractable real-time visual gesture recognition method based on FPGA, which can detect both static hand shapes and dynamic gestures. In paper [10], dual cameras were used to construct 3D images for gesture recognition, and VLSI was used to implement the system. The whole system was also verified on FPGA.…”

Section: Introductionmentioning

confidence: 99%

Research and implementation of dynamic gesture recognition system based on ZYNQ

Liu

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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“…The resolution of the videos is 1920 × 1080, and the frame rate is 15 frames per second. The dataset includes the basic ac-tions of a single person, which are classified as shooting, layup, dribbling, running dribbling, dunking, and running without the ball [20]. It also includes basic actions of multiple people, such as dribbling breakthrough and defense;.…”

Section: Methodsmentioning

confidence: 99%

Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network

Zuo

2022

Electronics

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This study proposes a 3D attitude estimation algorithm using the RMPE algorithm coupled with a deep neural network that combines human pose estimation and action recognition, which provides a new idea for basketball auxiliary training. Compared with the traditional single-action recognition method, the present method makes the recognition accuracy better and the display effect more intuitive. The flipped classroom teaching mode based on this algorithm is applied to the college sports basketball optional course to explore the influence of this teaching mode on the classroom teaching effect. Compared with the evaluation index of action recognition, the experimental results of various action recognition methods and datasets are compared and analyzed, and it is verified that the method has a good recognition effect. The values of Topi and Top5 of the proposed method are 42.21% and 88.77%, respectively, which are 10.61% and 35.09% higher than those of the Kinetics-skeleton dataset. However, compared with the NTU RGM dataset, the recognition rate of Topi is significantly reduced. Compared with the traditional single-action recognition method, this method has better recognition accuracy and a more intuitive display effect. The fusion method of human posture estimation and motion recognition provides a new idea for basketball auxiliary training.

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“…A sub-category of HAR is the recognition of hand gestures that can be exploited to control home appliances and robots or to assist in communication with people who are deaf and people who cannot speak. In [ 6 ], a system with dual cameras was proposed for both static and dynamic gesture recognition. The authors propose a hardware architecture that improves execution speed while maintaining high efficiency.…”

Section: Overview Of Contributionmentioning

confidence: 99%