A Video Gesture Processing Method Based on Convolution and Long Short-Term Memory Network

Ding, Xiaoxue; Chen, Xu; Yan, Qing

doi:10.1109/icccbda.2019.8725619

Cited by 3 publications

(2 citation statements)

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“…In [103], the authors use optical flow, CNN, LSTM, and support vector machine (SVM) for gesture recognition from video data. This approach is highly applicable for decoding the news for the deaf-mute community.…”

Section: ) Hybrid Approachmentioning

confidence: 99%

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

et al. 2021

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Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc. However, a combined study is still unexplored. This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by formulating the relevant research questions (RQs). This systematic mapping includes 93 research articles from reputed databases published between 2011 and 2020. We categorize the deep learning technique for video processing as CNN, DNN, and RNN based. We observe the significant advancements in video processing between 2017 and 2020, primarily due to the advent of AlexNet, ResNet, and LSTM based deep learning techniques. The prominent fields of video processing research are observed as human action recognition, crowd anomaly detection, and behavior analysis. This SLR is a helpful guide for the researchers to explore the recent literature, available datasets, and existing deep learning techniques for video processing.

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Section: ) Hybrid Approachmentioning

confidence: 99%

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

et al. 2021

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“…The authors of [4] utilized skeleton as input data for model network that is similar to proposed architecture in [1]. The model CNN + LSTM is also used in [5], so that the authors put stacked optical flow into network. In [6,7], the authors used 3D-CNN architecture to learn spatio-temporal information for hand gesture recognition model.…”

Section: Introductionmentioning

confidence: 99%

Two-Stream Convolutional Network for Dynamic Hand Gesture Recognition Using Convolutional Long Short-Term Memory Networks

Nguyen

Luong

2020

JST

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Human action and gesture recognition provides important and worth information for interaction between human and device ambient that monitors living, healthcare facilities or entertainment activities in smart homes. Recent years, there were many machine learning model application studies to recognize human action and gesture. In this paper, we propose a dynamic hand gesture recognition system in video based on two stream-convolution network (ConvNet) architecture. Specifically, we research the state-of-the-art approaches using to recognize dynamic hand gesture in video and propose an improvement method to enhance performance of model which is suitable for uses such as indoor environment in this paper. Our contribution is improvement of two stream ConvNet to achieve better performance. The results show that the proposal model improves execution speed and memory resource usage comparing to existing models.

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