NDLI: Efficient inception V2 based deep convolutional neural network for real-time hand action recognition

Content Provider	IET Digital Library
Author	Bose, S. Rubin Kumar, V. Sathiesh
Abstract	The most effective and accurate deep convolutional neural network (faster region-based convolutional neural network (Faster R-CNN) Inception V2 model, single shot detector (SSD) Inception V2 model) based architectures for real-time hand gesture recognition is proposed. The proposed models are tested on standard data sets (NUS hand posture data set-II, Senz-3D) and custom-developed (MITI hand data set (MITI-HD)) data set. The performance metrics are analysed for intersection over union (IoU) ranges between 0.5 and 0.95. IoU value of 0.5 resulted in higher precision compared to other IoU values considered (0.5:0.95, 0.75). It is observed that the Faster R-CNN Inception V2 model resulted in higher precision (0.990 for APall, IoU = 0.5) compared to SSD Inception V2 model (0.984 for all) for MITI-HD 160. The computation time of Faster R-CNN Inception V2 is higher compared to SSD Inception V2 model and also resulted in less number of mispredictions. Increasing the size of samples (MITI-HD 300) resulted in improvement of APall = 0.991. Improvement in large (APlarge) and medium (APmedium) size detections are not significant when compared to small (APsmall) detections. It is concluded that the Faster R-CNN Inception V2 model is highly suitable for real-time hand gesture recognition system under unconstrained environments.
Starting Page	688
Ending Page	696
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 4, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0985
Journal	IET Image Processing
Publisher Date	2019-11-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Accurate Deep Convolutional Neural Network Computer Vision And Image Processing Technique Convolution Effective Network Efficient Inception V2 Faster R-CNN Inception V2 Model Faster Region-based Convolutional Neural Network Feature Extraction Gesture Recognition Higher Precision Image Classification Image Recognition IoU Value Knowledge Engineering Technique Learning in AI MITI-HD 160 Neural Computing Technique Neural Nets NUS Hand Posture Data Set-II Real-time Hand Action Recognition Real-time Hand Gesture Recognition System SSD Inception V2 Model Standard Data Sets
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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