Vision-Based Approach for Real-Time Hand Detection and Gesture Recognition

2018 ◽  
pp. 67-81
Author(s):  
Rayane El Sibai ◽  
Chady Abou Jaoude ◽  
Jacques Demerjian
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Hand Detection

A Real-Time Dynamic Gesture Recognition System

2013 ◽  
Vol 333-335 ◽  
pp. 849-855 ◽  
Author(s):  
Jiang Guo ◽  
Jun Cheng ◽  
Yu Guo ◽  
Jian Xin Pang
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Sensory Information ◽  
Recognition System ◽  
Correction Method ◽  
Hand Detection ◽  
Time Dynamic ◽  
Trajectory Correction ◽  
Dynamic Time ◽  
Dynamic Gesture Recognition

In this paper, we present a dynamic gesture recognition system. We focus on the visual sensory information to recognize human activity in form of hand movements from a small, predefined vocabulary. A fast and effective method is presented for hand detection and tracking at first for the trajectory extraction. A novel trajectory correction method is applied for simply but effectively trajectory correction. Gesture recognition is achieved by means of a matching technique by determining the distance between the unknown input direction code sequence and a set of previously defined templates. A dynamic time warping (DTW) algorithm is used to perform the time alignment and normalization by computing a temporal transformation allowing the two signals to be matched. Experiment results show our proposed gesture recognition system achieve well result in real time.

A Model for Real-Time Hand Gesture Recognition Using Electromyography (EMG), Covariances and Feed-Forward Artificial Neural Networks

2020 ◽  
Author(s):  
Marco E. Benalcazar ◽  
Jose Gonzalez ◽  
Andres Jaramillo-Yanez ◽  
Carlos E. Anchundia ◽  
Patricio Zambrano ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Feed Forward ◽  
Artificial Neural

A Low-Cost, IMU-Based Real-Time On Device Gesture Recognition Glove

2020 ◽  
Author(s):  
Oleg Makaussov ◽  
Mikhail Krassavin ◽  
Maxim Zhabinets ◽  
Siamac Fazli
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Low Cost

scholarly journals Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps

2021 ◽  
Vol 11 (4) ◽  
pp. 1933
Author(s):  
Hiroomi Hikawa ◽  
Yuta Ichikawa ◽  
Hidetaka Ito ◽  
Yutaka Maeda
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Weight Vector ◽  
Recognition System ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Dynamic ◽  
System Input ◽  
Self Organizing ◽  
Gesture Spotting

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result.

scholarly journals Robust hand gesture recognition using multiple shape-oriented visual cues

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Pose Estimation ◽  
Depth Map ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Hand Pose Estimation ◽  
Time Operation ◽  
Hand Pose

AbstractRobust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation.

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