scholarly journals Real Time Human Gesture Recognition: Methods, Datasets and Strategies

2021 ◽  
Author(s):  
Rameez Shamalik ◽  
Sanjay Koli
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Real Time ◽  
Sign Language ◽  
Gesture Recognition ◽  
Three Dimensional ◽  
Language Barrier ◽  
Key Steps ◽  
Hand Action

Gestures are universal means of communication without any language barrier. Detecting gestures and recognition of its meaning are key steps for researchers in computer vision. Majority of the work is done in sign language already. Sign language datasets are compared with respect to their usability and diversity in terms of various signs. This paper highlights the available datasets from three dimensional body scans to hand action gestures. Their usability and strategies used to achieve the desired results are also discussed. Major neural networks are evaluated in terms of varied parameters and feutures. A Methodology for effective gesture recognition in real is proposed. Lastly Results achieved through an Open CV in combination with Sci-kit learn library based technique for gesture recognition are presented and analyzed in terms of efficacy and efficiency.

scholarly journals Indian Sign Language Recognition through Hybrid ConvNet-LSTM Networks

2021 ◽  
Vol 9 (1) ◽  
pp. 182-203
Author(s):  
Muthu Mariappan H ◽  
Dr Gomathi V
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Real Time ◽  
Sign Language ◽  
Gesture Recognition ◽  
Language Translation ◽  
Video Gaming ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Indian Sign Language

Dynamic hand gesture recognition is a challenging task of Human-Computer Interaction (HCI) and Computer Vision. The potential application areas of gesture recognition include sign language translation, video gaming, video surveillance, robotics, and gesture-controlled home appliances. In the proposed research, gesture recognition is applied to recognize sign language words from real-time videos. Classifying the actions from video sequences requires both spatial and temporal features. The proposed system handles the former by the Convolutional Neural Network (CNN), which is the core of several computer vision solutions and the latter by the Recurrent Neural Network (RNN), which is more efficient in handling the sequences of movements. Thus, the real-time Indian sign language (ISL) recognition system is developed using the hybrid CNN-RNN architecture. The system is trained with the proposed CasTalk-ISL dataset. The ultimate purpose of the presented research is to deploy a real-time sign language translator to break the hurdles present in the communication between hearing-impaired people and normal people. The developed system achieves 95.99% top-1 accuracy and 99.46% top-3 accuracy on the test dataset. The obtained results outperform the existing approaches using various deep models on different datasets.

Hand gesture recognition by means of region-based convolutional neural networks

2017 ◽  
Vol 10 (27) ◽  
pp. 1329-1342 ◽  
Author(s):  
Javier O. Pinzon Arenas ◽  
Robinson Jimenez Moreno ◽  
Paula C. Useche Murillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
The Neural Network ◽  
Average Accuracy

This paper presents the implementation of a Region-based Convolutional Neural Network focused on the recognition and localization of hand gestures, in this case 2 types of gestures: open and closed hand, in order to achieve the recognition of such gestures in dynamic backgrounds. The neural network is trained and validated, achieving a 99.4% validation accuracy in gesture recognition and a 25% average accuracy in RoI localization, which is then tested in real time, where its operation is verified through times taken for recognition, execution behavior through trained and untrained gestures, and complex backgrounds.

scholarly journals Comparative Performance Analysis of Neural Network Real-Time Object Detections in Different Implementations

2020 ◽  
Vol 226 ◽  
pp. 02020
Author(s):  
Alexey V. Stadnik ◽  
Pavel S. Sazhin ◽  
Slavomir Hnatic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Performance Analysis ◽  
Object Detection ◽  
Real Time ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Comparative Performance

The performance of neural networks is one of the most important topics in the field of computer vision. In this work, we analyze the speed of object detection using the well-known YOLOv3 neural network architecture in different frameworks under different hardware requirements. We obtain results, which allow us to formulate preliminary qualitative conclusions about the feasibility of various hardware scenarios to solve tasks in real-time environments.

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