Real-time gesture recognition using bio inspired 3D vision sensor

2012 ◽  
Author(s):  
Bernhard Kohn ◽  
Ahmed Nabil Belbachir ◽  
Aneta Nowakowska
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Vision Sensor ◽  
3D Vision

scholarly journals Real-Time Capture of Snowboarder’s Skiing Motion Using a 3D Vision Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhipeng Li ◽  
Jun Wang ◽  
Tao Zhang ◽  
Dave Balne ◽  
Bing Li ◽  
...  
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
High Speed ◽  
Vision Sensor ◽  
Virtual Character ◽  
3D Vision ◽  
Fast Speed ◽  
Capture Method ◽  
Tof Camera ◽  
Motion Images

Due to the influence of environmental interference and too fast speed, there are some problems in ski motion capture, such as inaccurate motion capture, motion delay, and motion loss, resulting in the inconsistency between the actual motion of later athletes and the motion of virtual characters. To solve the above problems, a real-time skiing motion capture method of snowboarders based on a 3D vision sensor is proposed. This method combines the Time of Fight (TOF) camera and high-speed vision sensor to form a motion acquisition system. The collected motion images are fused to form a complete motion image, and the pose is solved. The pose data is bound with the constructed virtual character model to drive the virtual model to synchronously complete the snowboarding motion and realize the real-time capture of skiing motion. The results show that the motion accuracy of the system is as high as 98.6%, which improves the capture effect, and the motion matching proportion is better and more practical. It is also excellent in the investigation of motion delay and motion loss.

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.

Sign in / Sign up

Export Citation Format

Share Document