Empirical Study of a Complete System for Real-Time Face Pose Estimation

2010 ◽  
pp. 129-162
Author(s):  
Tommaso Gritti
Keyword(s):  
Empirical Study ◽  
Real Time ◽  
Pose Estimation ◽  
Complete System

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.

scholarly journals Real-Time Human Pose Estimation and Gesture Recognition from Depth Images Using Superpixels and SVM Classifier

Sensors ◽  
2015 ◽  
Vol 15 (6) ◽  
pp. 12410-12427 ◽  
Author(s):  
Hanguen Kim ◽  
Sangwon Lee ◽  
Dongsung Lee ◽  
Soonmin Choi ◽  
Jinsun Ju ◽  
...  
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Pose Estimation ◽  
Human Pose Estimation ◽  
Svm Classifier ◽  
Depth Images ◽  
Human Pose

HBE: Hand Branch Ensemble Network for Real-Time 3D Hand Pose Estimation

2018 ◽  
pp. 521-536 ◽  
Author(s):  
Yidan Zhou ◽  
Jian Lu ◽  
Kuo Du ◽  
Xiangbo Lin ◽  
Yi Sun ◽  
...  
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Hand Pose Estimation ◽  
Hand Pose

Real-Time 6DOF Pose Estimation of Endoscopic Instruments Using Printable Markers

2019 ◽  
Vol 19 (6) ◽  
pp. 2338-2346 ◽  
Author(s):  
Aniket Gadwe ◽  
Hongliang Ren
Keyword(s):  
Real Time ◽  
Pose Estimation

scholarly journals Learning-Type Anchors-Driven Pose Estimation for the Autolanding Fixed-Wing UAVs

2021 ◽  
Author(s):  
Dengqing Tang ◽  
Lincheng Shen ◽  
Xiaojiao Xiang ◽  
Han Zhou ◽  
Tianjiang Hu
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Stereo Vision ◽  
Vision System ◽  
Estimation Method ◽  
Satellite System ◽  
Training Dataset ◽  
Estimation Accuracy ◽  
The Impact ◽  
Outdoor Experiments

<p>We propose a learning-type anchors-driven real-time pose estimation method for the autolanding fixed-wing unmanned aerial vehicle (UAV). The proposed method enables online tracking of both position and attitude by the ground stereo vision system in the Global Navigation Satellite System denied environments. A pipeline of convolutional neural network (CNN)-based UAV anchors detection and anchors-driven UAV pose estimation are employed. To realize robust and accurate anchors detection, we design and implement a Block-CNN architecture to reduce the impact of the outliers. With the basis of the anchors, monocular and stereo vision-based filters are established to update the UAV position and attitude. To expand the training dataset without extra outdoor experiments, we develop a parallel system containing the outdoor and simulated systems with the same configuration. Simulated and outdoor experiments are performed to demonstrate the remarkable pose estimation accuracy improvement compared with the conventional Perspective-N-Points solution. In addition, the experiments also validate the feasibility of the proposed architecture and algorithm in terms of the accuracy and real-time capability requirements for fixed-wing autolanding UAVs.</p>

Sign in / Sign up

Export Citation Format

Share Document