Human gesture recognition on product manifolds

By Yui Man Lui. 2012

99 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering

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

In Control and Systems Engineering

1. Peiyi Shen
2. Jian Cheng
3. Feng Jiang
4. Jun Wan
5. Chang-Fu Tang
6. Wanqing Li
7. Hanqing Lu
8. Philip O. Ogunbona

Citations in the corpus

• One-shot-learning gesture recognition using HOG-HOF features (2013, background): “Tensor representation of action videos is proposed by Lui (2012).”
• Online RGB-D gesture recognition with extreme learning machines (2013, methodology): “In [14] videos of different actions are considered as third order tensors and imposed on a product manifold.”
• Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition (2016, methodology): “Appearance-based method [11], [16], [38] is another way to achieve temporal segmentation, which find candidate cuts based on similarities with respect to the resting position or the amount of motion.”
• Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM (2017, methodology): “Lui [29] characterized gesture videos as points on a Grassmann manifold and adopted the least square regression method for gesture recognition.”
• Recent methods and databases in vision-based hand gesture recognition: A review (2015, methodology): “Lui [92, 99] proposed a gesture recognition algorithm based on a nonlinear regression framework on manifolds.”
• Challenges in multimodal gesture recognition (2016, methodology): “Three of these papers propose new methods for gesture and action recognition (Malgireddy et al., 2013; Fanello et al., 2013; Lui, 2012), that were also evaluated on parts of the CDG2011 dataset (Guyon et al.”
• ChaLearn looking at people: A review of events and resources (2017, background): “Topic at Journal of Machine Learning Research [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30] 2016 TPAMI HuPBA: We organized a Special Issue on Multi-modal Human Pose Recovery and Behavior Analysis (HuPBA) at IEEE Transactions on Pattern Ana”
• Multi-layered gesture recognition with Kinect (2015, methodology): “For the comparison on each batch, the proposed method is compared with a manifold and nonlinear regression based method (Manifold LSR) (Lui, 2012b), an extended motionhistory-image and correlation coefficient based method (Extended-MHI) (Wu et al., 2012a), and a motion silhouettes based method…”
• EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition (2018, methodology): “In [40], hands are assumed to return to a neutral pose between two gestures, and the correlation coefficient is calcu-”
• Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks (2018, methodology): “Upon the general assumption that the start and end frames of adjacent actions are similar, correlation coefficients [31] and K-nearest neighbour algorithm with histogram of oriented gradient (HOG) [32] are used to identify the start and end frames of actions.”