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 Understanding the hand-gestures using Convolutional Neural Networks and Generative Adversial Networks

2021 ◽  
Author(s):  
Arpita Vats
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Skin Color ◽  
Recognition System ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Camshift Algorithm ◽  
Hand Region

<p>In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, training gesture and gesture recognition using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis for hand tracking are being used to obtain motion descriptors and hand region. It is fairy robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, the techniques have been proposed to improve the performance of the recognition and the accuracy using the approaches like selection of the training images and the adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. In the experiments, it has been tested to the vocabulary of 36 gestures including the alphabets and digits, and results effectiveness of the approach.</p>

scholarly journals Understanding the hand-gestures using Convolutional Neural Networks and Generative Adversial Networks

2021 ◽  
Author(s):  
Arpita Vats
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Skin Color ◽  
Recognition System ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Camshift Algorithm ◽  
Hand Region

<p>In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, training gesture and gesture recognition using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis for hand tracking are being used to obtain motion descriptors and hand region. It is fairy robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, the techniques have been proposed to improve the performance of the recognition and the accuracy using the approaches like selection of the training images and the adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. In the experiments, it has been tested to the vocabulary of 36 gestures including the alphabets and digits, and results effectiveness of the approach.</p>

Ukrainian dactyl alphabet gesture recognition using cross platform software and convolutional neural networks

2019 ◽  
Vol 24 (3-4) ◽  
pp. 107-113
Author(s):  
Kondratiuk S.S. ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Training Dataset ◽  
Hand Model ◽  
Cross Platform ◽  
The Cross

The technology, which is implemented with cross platform tools, is proposed for modeling of gesture units of sign language, animation between states of gesture units with a combination of gestures (words). Implemented technology simulates sequence of gestures using virtual spatial hand model and performs recognition of dactyl items from camera input using trained on collected training dataset set convolutional neural network. With the cross platform means technology achieves the ability to run on multiple platforms without re-implementing for each platform

Ukrainian dactyl alphabet gesture recognition using convolutional neural networks with 3d convolutions

2019 ◽  
Vol 24 (1-2) ◽  
pp. 94-100
Author(s):  
Kondratiuk S.S. ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Training Dataset ◽  
Test Dataset ◽  
Hand Model ◽  
Cross Platform

The technology, which is implemented with cross platform tools, is proposed for modeling of gesture units of sign language, animation between states of gesture units with a combination of gestures (words). Implemented technology simulates sequence of gestures using virtual spatial hand model and performs recognition of dactyl items from camera input using trained on collected training dataset set convolutional neural network, based on the MobileNetv3 architecture, and with the optimal configuration of layers and network parameters. On the collected test dataset accuracy of over 98% is achieved.

scholarly journals Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot

2021 ◽  
Vol 11 (21) ◽  
pp. 10043
Author(s):  
Claudia Álvarez-Aparicio ◽  
Ángel Manuel Guerrero-Higueras ◽  
Luis V. Calderita ◽  
Francisco J. Rodríguez-Lera ◽  
Vicente Matellán ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mobile Robot ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Large Datasets ◽  
Lidar Data ◽  
Labelling Process ◽  
Real Time Location System

Convolutional Neural Networks are usually fitted with manually labelled data. The labelling process is very time-consuming since large datasets are required. The use of external hardware may help in some cases, but it also introduces noise to the labelled data. In this paper, we pose a new data labelling approach by using bootstrapping to increase the accuracy of the PeTra tool. PeTra allows a mobile robot to estimate people’s location in its environment by using a LIDAR sensor and a Convolutional Neural Network. PeTra has some limitations in specific situations, such as scenarios where there are not any people. We propose to use the actual PeTra release to label the LIDAR data used to fit the Convolutional Neural Network. We have evaluated the resulting system by comparing it with the previous one—where LIDAR data were labelled with a Real Time Location System. The new release increases the MCC-score by 65.97%.

scholarly journals Real Time Attendance System using Convolutional Neural Networks(CNN)

2019 ◽  
Vol 9 (2) ◽  
pp. 3942-3946
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Security ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Face Detection ◽  
The People ◽  
Perfect Model ◽  
Term Paper

The management of the attendance can be an incredible weight on the instructors in the event that it is completed in registers. Determining this issue, keen and automatic attendance marking system by using the executive’s framework is being used. In any case, verification is a significant problem in this framework. Brilliant attendance framework is implemented commonly along with the assistance of soft biometrics. Acknowledgment of face is one of the updated biometric techniques this framework got to be enhanced. Being a principle element of biometric confirmation, facial acknowledgment feature has become most utilized enormously in a few such applications, similar to video observing and surveillance-based CCTV film framework, a connection between PC and people and admittance frameworks existing inside and in network security. By using this structure, the issue present in along with intermediaries, understudies also have been checking on the present despite the fact that they are not physically present can without much of a stretch be illuminated. The primary usage steps utilized regarding this sort of framework are facial discovery and perceiving the distinguished the different face of the people. This term paper recommends a perfect model for actualizing a computerized attendance the board framework in order to make understudies for a class by utilizing the procedure of acknowledgment-based face detection procedure, by means of utilizing Convolutional Neural Network (CNN), Max pooling

scholarly journals Online Hand Gesture Recognition and Classification for Deaf and Dumb

2021 ◽  
Vol 9 (VI) ◽  
pp. 4402-4404
Author(s):  
U. Mamatha
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
Gesture Recognition ◽  
Reference Image ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Network Layer ◽  
Web Camera ◽  
The Web

As sign language is used by deaf and dumb but the non-sign-language speaker cannot understand there sign language to overcome the problem we proposed this system using python. In this first we taken the some of the hand gestures are captured using the web camera. The image is pre-processed and then feature are extracted from the captured image .comparing the feature extracted image with the reference image. If matched decision is taken the displayed as a text. This helps the non-sign-language members to recognize easily by using Convolutional neural network layer (CNN) with tensor flow

scholarly journals Convolutional Neural Network Based Approach to Detect Pedestrians in Real-Time videos

2020 ◽  
Vol 10 (1) ◽  
pp. 303-308
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Prediction Accuracy ◽  
Video Quality ◽  
Test Set ◽  
High Prediction ◽  
The Given

Pedestrians in the vehicle way are in peril of being hit, along these lines making extreme damage walkers and vehicle inhabitants. Hence, constant person on foot identification was done through a set of recorded videos and the system detects the persons/pedestrians in the given input videos. In this survey, a continuous plan was proposed dependent on Aggregated Channel Features (ACF) and CPU. The proposed technique doesn't have to resize the information picture neither the video quality. We also use SVM with HOG and SVM with HAAR to detect the pedestrians. In addition, the Convolutional Neural Networks (CNN) were trained with a set of pedestrian images datasets and later tested on some test-set of pedestrian images. The analyses demonstrated that the proposed technique could be utilized to distinguish people on foot in the video with satisfactory mistake rates and high prediction accuracy. In this manner, it tends to be applied progressively for any real-time streaming of videos and also for prediction of pedestrians in prerecorded videos.

scholarly journals Convolutional neural networks framework for human hand gesture recognition

2021 ◽  
Vol 10 (4) ◽  
pp. 2223-2230
Author(s):  
Aseel Ghazi Mahmoud ◽  
Ahmed Mudheher Hasan ◽  
Nadia Moqbel Hassan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Hand ◽  
Language Recognition ◽  
Hand Gestures ◽  
Recognition Systems

Recently, the recognition of human hand gestures is becoming a valuable technology for various applications like sign language recognition, virtual games and robotics control, video surveillance, and home automation. Owing to the recent development of deep learning and its excellent performance, deep learning-based hand gesture recognition systems can provide promising results. However, accurate recognition of hand gestures remains a substantial challenge that faces most of the recently existing recognition systems. In this paper, convolutional neural networks (CNN) framework with multiple layers for accurate, effective, and less complex human hand gesture recognition has been proposed. Since the images of the infrared hand gestures can provide accurate gesture information through the low illumination environment, the proposed system is tested and evaluated on a database of hand-based near-infrared which including ten gesture poses. Extensive experiments prove that the proposed system provides excellent results of accuracy, precision, sensitivity (recall), and F1-score. Furthermore, a comparison with recently existing systems is reported.

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