Skip to search formSkip to main contentSkip to account menu

Mining actionlet ensemble for action recognition with depth cameras

An actionlet ensemble model is learnt to represent each action and to capture the intra-class variance, and novel features that are suitable for depth data are proposed.
View on IEEE (opens in a new tab)

Sparse reconstruction cost for abnormal event detection

The method provides a unified solution to detect both local abnormal events and global abnormal events through a sparse reconstruction over the normal bases and extends it to support online abnormal event detection by updating the dictionary incrementally.
View on IEEE (opens in a new tab)

Learning Actionlet Ensemble for 3D Human Action Recognition

This paper proposes to characterize the human actions with a novel actionlet ensemble model, which represents the interaction of a subset of human joints, which is robust to noise, invariant to translational and temporal misalignment, and capable of characterizing both the human motion and the human-object interactions.
View on IEEE (opens in a new tab)

Robust Part-Based Hand Gesture Recognition Using Kinect Sensor

A novel distance metric, Finger-Earth Mover's Distance (FEMD), is proposed, which only matches the finger parts while not the whole hand, it can better distinguish the hand gestures of slight differences.
View on IEEE (opens in a new tab)

Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera

A novel distance metric for hand dissimilarity measure, called Finger-Earth Mover's Distance (FEMD), which only matches fingers while not the whole hand shape, can better distinguish hand gestures of slight differences.
View on ACM (opens in a new tab)

3D Hand Shape and Pose Estimation From a Single RGB Image

This work proposes a Graph Convolutional Neural Network (Graph CNN) based method to reconstruct a full 3D mesh of hand surface that contains richer information of both 3D hand shape and pose and proposes a weakly-supervised approach by leveraging the depth map as a weak supervision in training.
View on IEEE (opens in a new tab)

Fast action proposals for human action detection and search

Experimental results on two challenging datasets, MSRII and UCF 101, validate the superior performance of the action proposals as well as competitive results on action detection and search.
View on IEEE (opens in a new tab)

Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection

This work forms video summarization as a novel dictionary selection problem using sparsity consistency, where a dictionary of key frames is selected such that the original video can be best reconstructed from this representative dictionary.
View on IEEE (opens in a new tab)

3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation from Single Depth Images

Experiments show that the proposed 3D CNN based approach outperforms state-of-the-art methods on two challenging hand pose datasets, and is very efficient as the implementation runs at over 215 fps on a standard computer with a single GPU.
View on IEEE (opens in a new tab)

Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs

This work proposes to first project the query depth image onto three orthogonal planes and utilize these multi-view projections to regress for 2D heat-maps which estimate the joint positions on each plane to produce final 3D hand pose estimation with learned pose priors.
View on IEEE (opens in a new tab)
...
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)