One shot learning gesture recognition from RGBD images

By Di Wu, Fan Zhu, Ling Shao. 2012

151 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Computer Interaction

How researchers see this paper

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

In Artificial Intelligence

1. Wanqing Li
2. Pichao Wang
3. Philip O. Ogunbona
4. Zhimin Gao
5. Jun Wan
6. Isabelle M Guyon
7. Chang-Fu Tang
8. Herman Kamper

In Computer Vision and Pattern Recognition

1. Pichao Wang
2. Wanqing Li
3. Philip O. Ogunbona
4. Zhimin Gao
5. Chang-Fu Tang
6. Jun Wan
7. Sergio Escalera
8. S. Li

In Human-Computer Interaction

1. Pichao Wang
2. Wanqing Li
3. Philip O. Ogunbona
4. Jun Wan
5. Zhimin Gao
6. Chang-Fu Tang
7. Isabelle M Guyon
8. Sergio Escalera

Citations in the corpus

• One-shot-learning gesture recognition using HOG-HOF features (2013, methodology): “Wu et al. (2012) pre-segment videos and represent motions of users by Extended-MotionHistory-Image and use a maximum correlation coefficient classifier.”
• Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks (2018, methodology): “Yang et al. [35] stacked differences between projected depth maps as a depth motion map (DMM) and then used HOG to extract relevant features from the DMM.”
• ChaLearn gesture challenge: Design and first results (2012, background): “The eighth ranked team (xiazhuwudi) published a paper in these proceedings entitled “One Shot Learning Gesture Recognition from RGBD Images” [9].”
• Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition (2016, methodology): “In previous works, the aim of preprocessing is to obtain more pure human body mask which will be used to extract more efficient features [11], [29], [33].”
• Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks (2016, methodology): “Upon the general assumption that the start and end frames of adjacent gestures are similar, correlation coefficients [24] and K-nearest neighbour algorithm with histogram of oriented gradient (HOG) [25] are used to identify the start and end frames of gestures.”
• Recent methods and databases in vision-based hand gesture recognition: A review (2015, background): “[90] D Kinect Extended-Motion-History-Image Maximum correlation coefficient”
• One-Shot Learning for Real-Time Action Recognition (2013, methodology): “Finally we report the results obtained by the recent works in the field, which exploit techniques based on manifolds [10], Motion History Image (MHI) [9] and Bag of Visual Words (BoVW) [9].”
• Multimodal One-shot Learning of Speech and Images (2018, background): “A limited number of studies have considered other domains, such as robotics [6, 26], video [27], and gesture recognition [8, 28].”
• Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning (2019, background): “[36] address one-shot learning in the context of path planning algorithms for robotic actuation.”
• Multi-layered gesture recognition with Kinect (2015, methodology): “Because the direct depth sensors are expensive, inexpensive depth cameras, e.g., Microsoft’s Kinect, have been recently used in gesture recognition (Ershaed et al., 2011; Wu et al., 2012b).”
• Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM (2017, methodology): “[28] extracted the extendedmotion history image (Extended-MHI) from RGB and depth sequences, and used maximum correlation coefficient to recognize gestures.”