WIRELESS CONTROL OF AN AUTOMOBILE USING AIR GESTURES

In the present world people with disabilities wish to be independent and thus the dependence on automatic machinery has increased drastically. People with physical disabilities and partial paralysis find it difficult to navigate without the assistance of someone. The system proposed will be a boost to the physically challenged people as it will help them to be self-reliable. Air gesture model is being as a key component to derive the benefit and gesture recognition is one obvious way to create a useful, highly adaptive interface between machines and their users. The gestures of the hand are read by a camera which is attached to a computer where further processing occurs. Hand gesture recognition technology allows for the navigation of an automobile using only a series of finger and hand movements, eliminating the need for physical contact between operator and machine. If the hand gesture is similar then Arduino is programmed such that the robotic car moves forward. This model can be extended to other areas such as efficient control by traffic corps, patients and senior citizens to call or interact with others, and so on. However, in this paper, the focus will be more as part of driving vehicles.

Download Full-text

A Study of Hand Gesture Segmentation Techniques for Sign Languages

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8439 ◽

2020 ◽

Vol 17 (4) ◽

pp. 1764-1769

Author(s):

S. Gobhinath ◽

T. Vignesh ◽

R. Pavankumar ◽

R. Kishore ◽

K. S. Koushik

Keyword(s):

Sign Language ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Morphological Processing ◽

Hand Gesture ◽

Hand Detection ◽

Hand Segmentation ◽

Human Gestures ◽

Gesture Segmentation ◽

Physically Challenged

This paper presents about an overview on several methods of segmentation techniques for hand gesture recognition. Hand gesture recognition has evolved tremendously in the recent years because of its ability to interact with machine. Mankind tries to incorporate human gestures into modern technologies like touching movement on screen, virtual reality gaming and sign language prediction. This research aims towards employed on hand gesture recognition for sign language interpretation as a human computer interaction application. Sign Language which uses transmits the sign patterns to convey meaning by hand shapes, orientation and movements to fluently express their thoughts with other person and is normally used by the physically challenged people who cannot speak or hear. Automatic Sign Language which requires robust and accurate techniques for identifying hand signs or a sequence of produced gesture to help interpret their correct meaning. Hand segmentation algorithm where segmentation using different hand detection schemes with required morphological processing. There are many methods which can be used to acquire the respective results depending on its advantage.

Download Full-text

Hand Gesture Recognition system for physically challenged people using IoT

2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) ◽

10.1109/icaccs48705.2020.9074226 ◽

2020 ◽

Author(s):

Rajit Nair ◽

Dileep Kumar Singh ◽

Ashu ◽

Shivam Yadav ◽

Sourabh Bakshi

Keyword(s):

Gesture Recognition ◽

Recognition System ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Physically Challenged

Download Full-text

Dynamic Gesture Controlled User Interface Expert HCI System using Adaptative Background Masking: An Aid to Prevent Cross Infections

JOURNAL FOR CLINICAL AND DIAGNOSTIC RESEARCH ◽

10.7860/jcdr/2020/45065.13961 ◽

2020 ◽

Author(s):

Seema Rawat ◽

Praveen Kumar ◽

Ishita Singh ◽

Shourya Banerjee ◽

Shabana Urooj ◽

...

Keyword(s):

Computer Vision ◽

Gesture Recognition ◽

Video Processing ◽

Hand Gesture Recognition ◽

Physical Contact ◽

Hand Gesture ◽

Input Device ◽

Recognition Method ◽

Hand Gestures ◽

Dynamic Hand Gesture Recognition

Human-Computer Interaction (HCI) interfaces need unambiguous instructions in the form of mouse clicks or keyboard taps from the user and thus gets complex. To simplify this monotonous task, a real-time hand gesture recognition method using computer vision, image, and video processing techniques has been proposed. Controlling infections has turned out to be the major concern of the healthcare environment. Several input devices such as keyboards, mouse, touch screens can be considered as a breeding ground for various micro pathogens and bacteria. Direct use of hands as an input device is an innovative method for providing natural HCI ensuring minimal physical contact with the devices i.e., less transmission of bacteria and thus can prevent cross infections. Convolutional Neural Network (CNN) has been used for object detection and classification. CNN architecture for 3d object recognition has been proposed which consists of two models: 1) A detector, a CNN architecture for detection of gestures; and 2) A classifier, a CNN for classification of the detected gestures. By using dynamic hand gesture recognition to interact with the system, the interactions can be increased with the help of multidimensional use of hand gestures as compared to other input methods. The dynamic hand gesture recognition method focuses to replace the mouse for interaction with the virtual objects. This work centralises the efforts of implementing a method that employs computer vision algorithms and gesture recognition techniques for developing a low-cost interface device for interacting with objects in the virtual environment such as screens using hand gestures.

Download Full-text

Performance analysis and high recognition rate of automated hand gesture recognition though GMM and SVM-KNN classifiers

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/114952020 ◽

2020 ◽

Vol 9 (5) ◽

pp. 7712-7722

Keyword(s):

Performance Analysis ◽

Gesture Recognition ◽

Recognition Rate ◽

Hand Gesture Recognition ◽

Hand Gesture

Download Full-text

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/4/8 ◽

2020 ◽

Vol 17 (4) ◽

pp. 497-506

Author(s):

Sunil Patel ◽

Ramji Makwana

Keyword(s):

Neural Network ◽

Optical Flow ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Classification Model ◽

Hand Gesture ◽

Flow Data ◽

Dynamic Hand Gesture Recognition ◽

Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

Download Full-text