Effective Communication with Hand Gesture Recognition Using Voice Over and Crime Detection System for Disabled People

2020 ◽  
Vol 17 (11) ◽  
pp. 4934-4937
Author(s):  
Anitha Ponraj ◽  
Derangula Ajay Babu ◽  
Dasari Jagadish ◽  
R. Aroul Canessane ◽  
M. S. Roobini
Keyword(s):  
Gesture Recognition ◽  
Detection System ◽  
Current System ◽  
Criminal Activity ◽  
Effective Communication ◽  
Hand Gesture ◽  
Disabled People ◽  
Huge Number ◽  
Hand Motion ◽  
Crime Detection

The Hand motions are the most well-known types of correspondence and have extraordinary significance in our reality. They can help in building sheltered and agreeable UIs for a large number of uses. In the current system, we have to speak with the Deaf and moronic individuals utilizing Deaf and idiotic language just, there is no programmed device to change over that into sound arrangement. In the Proposed system, Hand motions are the most widely recognized types of correspondence and have incredible significance reality. It is used to build protected and agreeable UIs for huge number of uses. Various types of calculations have utilized on camera for hand motion acknowledgment, yet hearty implementation on motions from different subjects is as yet testing. In the Modification, We convey Hand motion acknowledgment alongside Criminal stance additionally to distinguish and anticipate any criminal activities by any client. So this application is utilized for Deaf and idiotic correspondence and voice over and well criminal activity discovery utilizing matlab.

Dynamic Hand Gesture Recognition Using Kinematic Features Based on Dynamic Time Warping

2012 ◽  
Vol 235 ◽  
pp. 68-73
Author(s):  
Hai Bo Pang ◽  
You Dong Ding
Keyword(s):  
Gesture Recognition ◽  
Dynamic Time Warping ◽  
Hand Gesture Recognition ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Direction Vector ◽  
Time Warping ◽  
Motion Direction ◽  
Hand Motion ◽  
Dynamic Time

Hand gesture provides an attractive alternative to cumbersome interface devices for human computer interface. Many hand gesture recognition methods using visual analysis have been proposed. In our research, we exploit multiple cues including divergence features, vorticity features and hand motion direction vector. Divergence and vorticity are derived from the optical flow for hand gesture recognition in videos. Then these features are computed by principal component analysis method. The hand tracking algorithm finds the hand centroids for every frame, computes hand motion direction vector. At last, we introduced dynamic time warping method to verify the robustness of our features. Those experimental results demonstrate that the proposed approach yields a satisfactory recognition rate.

Kinect-based hand gesture recognition using trajectory information, hand motion dynamics and neural networks

2019 ◽  
Vol 52 (1) ◽  
pp. 563-583 ◽  
Author(s):  
Fenglin Liu ◽  
Wei Zeng ◽  
Chengzhi Yuan ◽  
Qinghui Wang ◽  
Ying Wang
Keyword(s):  
Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Hand Motion ◽  
Motion Dynamics

Exploiting AdaRank Model and Trajectory of Hand Motion for Hand Gesture Recognition

2016 ◽  
Vol 14 (10) ◽  
pp. 1061-1065 ◽  
Author(s):  
Chin-Shyurng Fahn ◽  
Chang-Yi Kao ◽  
Ching-Bang Yao ◽  
Meng-Luen Wu
Keyword(s):  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Hand Motion

scholarly journals Hand Motion and Posture Recognition in a Network of Calibrated Cameras

2017 ◽  
Vol 2017 ◽  
pp. 1-25 ◽  
Author(s):  
Jingya Wang ◽  
Shahram Payandeh
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Motion Trajectory ◽  
Hand Posture ◽  
Scale Invariant ◽  
Hand Motion ◽  
Hand Posture Recognition ◽  
Posture Recognition

This paper presents a vision-based approach for hand gesture recognition which combines both trajectory and hand posture recognition. The hand area is segmented by fixed-range CbCr from cluttered and moving backgrounds and tracked by Kalman Filter. With the tracking results of two calibrated cameras, the 3D hand motion trajectory can be reconstructed. It is then modeled by dynamic movement primitives and a support vector machine is trained for trajectory recognition. Scale-invariant feature transform is employed to extract features on segmented hand postures, and a novel strategy for hand posture recognition is proposed. A gesture vector is introduced to recognize hand gesture as an entirety which combines the recognition results of motion trajectory and hand postures where a support vector machine is trained for gesture recognition based on gesture vectors.

Natural Gesture Recognition Based on Motion Detection and Skin Color

2013 ◽  
Vol 321-324 ◽  
pp. 974-979
Author(s):  
Kai Ping Feng ◽  
Ke Wan ◽  
Na Luo
Keyword(s):  
Gesture Recognition ◽  
Motion Detection ◽  
Skin Color ◽  
Object Motion ◽  
Hand Gesture ◽  
Color Analysis ◽  
Human Machine Interaction ◽  
Hand Motion ◽  
Color Features ◽  
Hand Region

With the development of the Virtual Reality technology and the next Human-Machine Interaction technology, this paper focus on the object motion detection and object skin color analysis, provide one kind of hand gesture segmentation method based on one camera. This method capture the image from the single camera to detect the moving object by the time difference method and the Gaussian module method, tracking the hand motion region real time, then to segment the hand gesture using the specified region skin color features after the hand region is extracted. Using the motion detection and the skin color features both, to do static gesture recognition by the template match method after extracting the features of the static gesture contour.This experiment make clear that the segmentation has better effect and recognition result.

scholarly journals Hand Gesture Recognition for Differently Abled People with Message Integration.

2020 ◽  
Vol 9 (4) ◽  
pp. 1432-1435
Keyword(s):  
Gesture Recognition ◽  
Region Of Interest ◽  
Neural System ◽  
Hand Gesture Recognition ◽  
Skin Colour ◽  
Background Information ◽  
Hand Gesture ◽  
Hand Motion

The objective of this paper is to utilize a webcam to lively track the region of interest (ROI), in particular, the hand locale, in the picture extend and recognize hand motion, we use skin colour discovery and also morphology to delete the unnecessary background information from the picture, and afterward use foundation subtraction to recognize the ROI. Next, to stay away from foundation effects on items or commotion influencing the ROI, we utilize the kernelized connection channels (KCF) calculation to follow the identified ROI. The picture size of the ROI is at that point resized to 28x28 and afterward sent into the profound convolutional neural system (CNN), so as to distinguish various hand signals. Two profound CNN designs are created right now are altered from DenseNet . At that point, the above procedure of following and acknowledgment is rehashed to accomplish a moment impact, and the framework's execution proceeds until the hand is removed from the camera.

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