Robust Part-Based Hand Gesture Recognition Using Kinect Sensor

By Zhou Ren, Junsong Yuan, Jingjing Meng, Zhengyou Zhang. IEEE transactions on multimedia, 2013

734 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/b2505495600176faaa8d1387eca8f9294b66df14

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Junsong Yuan
2. Sergio Escalera
3. Jianfei Cai
4. Isabelle M Guyon
5. Luc Van Gool
6. Dacheng Tao
7. Zicheng Liu
8. Vassilis Athitsos

In Control and Systems Engineering

1. Sergio Escalera
2. Isabelle M Guyon
3. Vassilis Athitsos
4. Chong Wang
5. Zhong Liu
6. Shing-Chow Chan
7. Jianfei Cai
8. Liuhao Ge

In Human-Computer Interaction

1. Sergio Escalera
2. Isabelle M Guyon
3. Junsong Yuan
4. Vassilis Athitsos
5. Chong Wang
6. Zhong Liu
7. Shing-Chow Chan
8. Jianfei Cai

Citations in the corpus

• Weakly-Supervised 3D Hand Pose Estimation from Monocular RGB Images (2018, positive): “Inspired by the great improvement of CNN-based 3D hand pose estimation from depth images[24], deep learning has also been adopted in some recent works on monocular RGB-based applications [46, 18].”
• Challenges in multimodal gesture recognition (2016, methodology): “The authors of (Ren et al., 2013) propose a part-based hand gesture recognition system using KinectT Msensor.”
• Superpixel-Based Hand Gesture Recognition With Kinect Depth Camera (2015, methodology): “We now evaluate and compare the proposed hand gesture recognition system with various state-of-the-art recognition algorithms including Shape Context [27], Skeleton Matching [26], FEMD [12], Random Forest (RF) [32], HOG [24] and H3DF [23], using three different real world datasets, namely our joint ”
• Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs (2016, methodology): “Index Terms—3D hand pose estimation, Convolutional neural networks, Multi-view CNNs F”
• Survey on 3D Hand Gesture Recognition (2016, methodology): “After hand detection and tracking, either static hand gesture recognition [68]–[70]”
• Parsing the Hand in Depth Images (2014, background): “While a lot of work has been done [2]–[5], [24]–[26], robust hand pose tracking and gesture recognition with visual inputs remains a challenging problem and the performance achieved cannot compare to the alternative methods, such as the dataglove based approaches [22].”
• Multi-layered gesture recognition with Kinect (2015, methodology): “…to the appealing performance and also reasonable cost, it has been widely used in several vision tasks such as face tracking (Cai et al., 2010), hand tracking (Oikonomidis et al., 2011), human action recognition (Wang et al., 2012) and gesture recognition (Doliotis et al., 2011; Ren et al., 2013).”
• A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training (2019, background): “This nature makes SL recognition an ideal research field for developing methods to address problems such as human motion analysis, human-computer interaction (HCI) and user interface design, and makes it receive great attention in multimedia and computer vision [2]–[4].”