An Integrative Approach to Robust Hand Detection Using CPM-YOLOv3 and RGBD Camera in Real Time

Raza, Muhammad Owais; Ketsoi, Vachiraporn; Chen, Haopeng

doi:10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00161

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…The multi-hand sign recognition is a hotspot research in vision based applications of human-machine communication frameworks. The real-time applications require a precise recognition of hand sign for an efficient and smooth control over the GUI [3]. Hand sign recognition offer a wide variety of applications such as human-computer communication, sign language understanding, robotic arm control, self-driving vehicles and telemetry frameworks for surgical applications [4].…”

Section: Introductionmentioning

confidence: 99%

Precise Recognition of Vision Based Multi-hand Signs Using Deep Single Stage Convolutional Neural Network

Bose

Kumar

2021

Communications in Computer and Information Science

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The precise recognition of multi-hand signs in real-time under dynamic backgrounds, illumination conditions is a time consuming process. In this paper, a time efficient single stage convolutional neural network (CNN) You Only Look Once (YOLO-V2) model is proposed for real-time multi-hand sign recognition. The model utilizes DarkNet-19 CNN architecture as a feature extractor. The model is trained and tested on three distinct datasets (NUSHP-II, SENZ-3D and MITI-HD). The range of IoU from 0.5 to 0.95, the model is validated using test dataset. For the MITI-HD, the YOLO-V2 CNN model obtained an average precision value of 99.10% for AP 0.5 ; 93.00% for AP 0.75 and 78.30% for AP 0.5:0.95 . The Adam Optimizer on YOLO-V2 CNN model supersedes the other optimization methods. The prediction time of YOLO-V2 CNN is obtained as 20 ms, much lower than other single-stage hand sign recognition systems.

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Section: Introductionmentioning

confidence: 99%