Virtual Gaming Using Gesture Recognition Model

2022 ◽  
pp. 114-124
Author(s):  
Swati Singhvi ◽  
Naman Gupta ◽  
Shashank Mouli Satapathy
Keyword(s):  
Gesture Recognition ◽  
Recognition Model

scholarly journals Towards Location Independent Gesture Recognition with Commodity WiFi Devices

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1069
Author(s):  
Yong Lu ◽  
Shaohe Lv ◽  
Xiaodong Wang
Keyword(s):  
Activity Recognition ◽  
Gesture Recognition ◽  
Recognition System ◽  
Low Rank ◽  
Background Information ◽  
Recognition Model ◽  
Sparse Decomposition ◽  
Average Accuracy

Recently, WiFi-based gesture recognition has attracted increasing attention. Due to the sensitivity of WiFi signals to environments, an activity recognition model trained at a specific place can hardly work well for other places. To tackle this challenge, we propose WiHand, a location independent gesture recognition system based on commodity WiFi devices. Leveraging the low rank and sparse decomposition, WiHand separates gesture signal from background information, thus making it resilient to location variation. Extensive evaluations showed that WiHand can achieve an average accuracy of 93% for various locations. In addition, WiHand works well under through the wall scenario.

scholarly journals Implementation and Performance Analysis of Different Hand Gesture Recognition Methods

2019 ◽  
pp. 13-20
Author(s):  
Md. Manik Ahmed ◽  
Md. Anwar Hossain ◽  
A F M Zainul Abadin
Keyword(s):  
Computer Vision ◽  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Optimization Technique ◽  
Light Condition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Model Accuracy ◽  
Recognition Model ◽  
Computer Interaction

In recent few years, hand gesture recognition is one of the advanced grooming technologies in the era of human computer interaction and computer vision due to a wide area of application in the real world. But it is a very complicated task to recognize hand gesture easily due to gesture orientation, light condition, complex background, translation and scaling of gesture images. To remove this limitation, several research works have developed which is successfully decrease this complexity. However, the intention of this paper is proposed and compared four different hand gesture recognition system and apply some optimization technique on it which ridiculously increased the existing model accuracy and model running time. After employed the optimization tricks, the adjusted gesture recognition model accuracy was 93.21% and the run time was 224 seconds which was 2.14% and 248 seconds faster than an existing similar hand gesture recognition model. The overall achievement of this paper could be applied for smart home control, camera control, robot control, medical system, natural talk, and many other fields in computer vision and human-computer interaction.

OPTIMAL AND FAST HAND GESTURE RECOGNITION MODEL USING FASTER R-CNN

2019 ◽  
Vol 12 ◽  
Author(s):  
Kathirvel Sundaramoorthy ◽  
Murugaboopathi G ◽  
Ramaprabha Marimuthu
Keyword(s):  
Gesture Recognition ◽  
Computation Time ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Important Research ◽  
Recognition Model ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
Important Research Topic ◽  
Efficient Recognition

Due to the advancements in computer vision, gesture recognition becomes an important research topic and is widely used for human-computer interfaces. Among gesture recognition models, the hand gesture is highly preferable because of its application in various applications like healthcare, gaming, etc. Though numerous hand gesture recognition models exist, none of these methods attained an efficient and faster detection rate in different situations. In this paper, we introduce an optimal and fast hand gesture recognition model using Faster R-CNN. The use of Faster R-CNN leads to efficient recognition at a faster rate. To evaluate the results of the Faster R-CNN model, we employ this model to a set of two benchmark hand gesture dataset. The experimental outcomes demonstrate that the Faster R-CNN model gains enhanced performance over the standard methods in terms of accuracy and computation time.

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