A Study of User Interface with Wearable Devices Based on Computer Vision

Hsu, Fang‐Rong; Kuo, Yu-Hsun; Wei, Shin-Yi; Hsieh, Yung-Hsien; Nguyen, Dinh Chien

doi:10.1109/mce.2019.2941463

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…The value of such an early failure warning is very high, as any downtime in a factory involves a huge cost, and it is impossible to have all replacements in the store. Additionally, there can be many similar consumer‐grade applications, such as adjusting salinity in aquariums, lighting control, intelligent home heating, and water heating control 27–29 …”

Section: Results Of Training and Computationmentioning

confidence: 99%

Darknet on OpenCL: A multiplatform tool for object detection and classification

Sowa¹,

Izydorczyk

2022

Concurrency and Computation

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The goal of this article is to overview the challenges and problems on the way from the state-of-the-art CUDA accelerated neural network code to multi-GPU code. For this purpose, the authors describe the journey of porting that existing in GitHub, a fully featured CUDA-accelerated Darknet engine, to OpenCL. This article presents the lessons learned and the techniques that were put in place for this porting. There are few other implementations on GitHub that leverage the OpenCL standard, and a few have tried to port Darknet as well. Darknet is a well-known convolutional neural network (CNN) framework. The authors of this article investigated all aspects of porting and achieved a fully featured Darknet engine on OpenCL. The effort was focused not only on classification using YOLO1, YOLO2, YOLO3, and YOLO4 CNN models.Other aspects were also covered, such as training neural networks and benchmarks to identify weak points in the implementation. Compared with the standard CPU version, the GPU computing code substantially improves the Darknet computing time by using underutilized hardware in existing systems. If the system is OpenCL-based, it is practically hardware-independent. The authors also improved the CUDA version as Darknet-vNext.

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Section: Results Of Training and Computationmentioning

confidence: 99%

Darknet on OpenCL: A multiplatform tool for object detection and classification

Sowa¹,

Izydorczyk

2022

Concurrency and Computation

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show abstract

“…e camera has the ability of color image acquisition, and its photosensitive array is 640 * 480, which has high sensitivity and is more suitable for low illumination shooting environment. e OV7725 camera has automatic adjustment function, which can effectively suppress the interference of noise points in the image [12,13].…”

Section: Fat Reduction Motion Image Acquisitionmentioning

confidence: 99%

Automatic Recognition for Fat Reduction Motion Image Landmark Using Computer Vision

Yang

Wang

et al. 2022

Mobile Information Systems

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To solve the problems existing in the traditional algorithm, such as image quality, the ability to predict the coordinate of the motion fat reduction image, the Intersection over Union (IOU) of the estimated coordinate of the landmark, the peak signal-to-noise ratio (PSNR) of the image, and the recognition accuracy of the image landmark, an automatic recognition algorithm for fat reduction motion image landmark using computer vision is proposed. The OV7725 camera is used to obtain the fat reduction motion image, and the histogram equalization method is used to enhance the fat reduction motion image. After that, the estimated coordinates of the landmark are obtained by the automatic recognition model of the landmarks of the fat reduction motion image. According to the coordinates of the landmark, after using a field programmable gate array to segment the image of the fat reduction motion image, the incremental updated freeman chain code extracts the contour of the landmark of the fat reduction motion image through 8-neighborhood retrieval, so as to realize the automatic recognition of landmark. The results show that the fat reduction motion image enhanced by the algorithm is clear and the contrast is obvious. The ability to predict the coordinates of the landmark of the fat reduction image is strong. The IOU of the estimated landmark coordinates is about 0.96. The processed image has a high PSNR, which can effectively recognize the landmark of the fat reduction motion image, and the recognition accuracy is about 97%, which has certain value in the field of image recognition and can improve the fat reduction movement normality.

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“…The value of such an early failure warning is very high, as any downtime in a factory is a huge loss, and it is prohibitively costly to have all replacement in the storage. Additionally, there can be many similar consumer-grade applications, such as adjusting salinity in aquariums, lighting control, intelligent home heating, water heating control, and much more [44][45][46].…”

Section: Example Of the Applicationmentioning

confidence: 99%

Darknet on OpenCL: A Multi-platform Tool for Object Detection and Classification

Sowa¹,

Izydorczyk²

2020

Preprint

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The article’s goal is to overview challenges and problems on the way from the state of the art CUDA accelerated neural networks code to multi-GPU code. For this purpose, the authors describe the journey of porting the existing in the GitHub, fully-featured CUDA accelerated Darknet engine to OpenCL. The article presents lessons learned and the techniques that were put in place to make this port happen. There are few other implementations on the GitHub that leverage the OpenCL standard, and a few have tried to port Darknet as well. Darknet is a well known convolutional neural network (CNN) framework. The authors of this article investigated all aspects of the porting and achieved the fully-featured Darknet engine on OpenCL. The effort was focused not only on the classification with the use of YOLO1, YOLO2, and YOLO3 CNN models. They also covered other aspects, such as training neural networks, and benchmarks to look for the weak points in the implementation. The GPU computing code substantially improves Darknet computing time compared to the standard CPU version by using underused hardware in existing systems. If the system is OpenCL-based, then it is practically hardware independent. In this article, the authors report comparisons of the computation and training performance compared to the existing CUDA-based Darknet engine in the various computers, including single board computers, and, different CNN use-cases. The authors found that the OpenCL version could perform as fast as the CUDA version in the compute aspect, but it is slower in memory transfer between RAM (CPU memory) and VRAM (GPU memory). It depends on the quality of OpenCL implementation only. Moreover, loosening hardware requirements by the OpenCL Darknet can boost applications of DNN, especially in the energy-sensitive applications of Artificial Intelligence (AI) and Machine Learning (ML).

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A Study of User Interface with Wearable Devices Based on Computer Vision

Cited by 11 publications

References 11 publications

Darknet on OpenCL: A multiplatform tool for object detection and classification

Darknet on OpenCL: A multiplatform tool for object detection and classification

Automatic Recognition for Fat Reduction Motion Image Landmark Using Computer Vision

Darknet on OpenCL: A Multi-platform Tool for Object Detection and Classification

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