The detection of an object such as a human is very important for image understanding in the field of computer vision Human detection in images can provide essential information for a wide variety of applications in intelligent systems In this paper, human detection is carried out using deep learning that has developed rapidly and achieved extraordinary success in various object detection implementations Recently, several embedded systems have emerged as powerful computing boards to provide high processing capabilities using the graphics processing unit (GPU) This paper aims to provide a comprehensive survey of the latest achievements in this field brought about by deep learning techniques in the embedded platforms NVIDIA Jetson was chosen as a low power system designed to accelerate deep learning applications This review highlights the performance of human detection models such as PedNet, multiped, SSD MobileNet V1, SSD MobileNet V2, and SSD inception V2 on edge computing This survey aims to provide an overview of these methods and compare their performance in accuracy and computation time for real-time applications The experimental results show that the SSD MobileNet V2 model provides the highest accuracy with the fastest computation time compared to other models in our video datasets with several scenarios

/pdf/real-time-human-detection-using-deep-learning-on-embedded-3iye69ty6e.pdf

Real-Time Human Detection Using Deep Learning on Embedded Platforms: A Review

Hand gesture recognition is viewed as a significant field of exploration in computer vision with assorted applications in the human-computer communication (HCI) community. The significant utilization of gesture recognition covers spaces like sign language, medical assistance and virtual reality-augmented reality and so on. The underlying undertaking of a hand gesture-based HCI framework is to acquire raw data which can be accomplished fundamentally by two methodologies: sensor based and vision based. The sensor-based methodology requires the utilization of instruments or the sensors to be genuinely joined to the arm/hand of the user to extract information. While vision-based plans require the obtaining of pictures or recordings of the hand gestures through a still/video camera. Here, we will essentially discuss vision-based hand gesture recognition with a little prologue to sensor-based data obtaining strategies. This paper overviews the primary methodologies in vision-based hand gesture recognition for HCI. Major topics include different types of gestures, gesture acquisition systems, major problems of the gesture recognition system, steps in gesture recognition like acquisition, detection and pre-processing, representation and feature extraction, and recognition. Here, we have provided an elaborated list of databases, and also discussed the recent advances and applications of hand gesture-based systems. A detailed discussion is provided on feature extraction and major classifiers in current use including deep learning techniques. Special attention is given to classify the schemes/approaches at various stages of the gesture recognition system for a better understanding of the topic to facilitate further research in this area.

/pdf/methods-databases-and-recent-advancement-of-vision-based-41gq0o7g7k.pdf

Methods, Databases and Recent Advancement of Vision-Based Hand Gesture Recognition for HCI Systems: A Review

One of the major challenges that deaf people face in modern societal life is communication. For those engaged in agricultural jobs, efficiency at work and productivity are deeply related to the quality of deciphering the sign language used by the deaf farmers. Employing sign language interpreters is not a pragmatic solution to this problem. There comes the need for developing a reliable system for automatic sign language recognition (SLR). This paper reports a work on the recognition of hand gestures for the Indian sign language (ISL) words commonly used by deaf farmers. A hybrid deep learning model with convolutional long short term memory (LSTM) network has been exploited for gesture classification. The model has attained an average classification accuracy of 76.21% on the proposed dataset of ISL words from the agricultural domain.

Applying deep neural networks for the automatic recognition of sign language words: A communication aid to deaf agriculturists

Automated Indian sign language recognition system by fusing deep and handcrafted feature

The coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. To refer government and enterprise to arrange countermeasures. The paper proposes a novel deep neural network framework to forecast the COVID-19 outbreak. The COVID-19Net framework combined 1D convolutional neural network, 2D convolutional neural network, and bidirectional gated recurrent units. COVID-19Net can well integrate the characteristics of time, space, and influencing factors of the COVID-19 accumulative cases. Three European countries with severe outbreaks were studied-Germany, Italy, and Spain-to extract spatiotemporal features and predict the number of confirmed cases. The prediction results acquired from COVID-19Net are compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which is commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which was considerably better than those of the other models. This indicated that the proposed framework could accurately predict the accumulated number of confirmed cases in the three countries and serve as an essential reference for devising public health strategies. And also indicated that COVID-19 has high spatiotemporal relations, it suggests us to keep a social distance and avoid unnecessary trips.

Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019.

Gesture recognition has been applied in many fields as it is a natural human–computer communication method. However, recognition of dynamic gesture is still a challenging topic because of complex disturbance information and motion information. In this paper, we propose an effective dynamic gesture recognition method by fusing the prediction results of a two-dimensional (2D) motion representation convolution neural network (CNN) model and three-dimensional (3D) dense convolutional network (DenseNet) model. Firstly, to obtain a compact and discriminative gesture motion representation, the motion history image (MHI) and pseudo-coloring technique were employed to integrate the spatiotemporal motion sequences into a frame image, before being fed into a 2D CNN model for gesture classification. Next, the proposed 3D DenseNet model was used to extract spatiotemporal features directly from Red, Green, Blue (RGB) gesture videos. Finally, the prediction results of the proposed 2D and 3D deep models were blended together to boost recognition performance. The experimental results on two public datasets demonstrate the effectiveness of our proposed method.

Fusion of 2D CNN and 3D DenseNet for Dynamic Gesture Recognition

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Fangzhou Cao

Author Tools

Chat about Author

Papers

Fusion of 2D CNN and 3D DenseNet for Dynamic Gesture Recognition