scholarly journals Compressive Sensing of Time Series for Human Action Recognition

2010 ◽  
Author(s):  
Oscar Perez Concha ◽  
Richard Yi Da Xu ◽  
Massimo Piccardi
Keyword(s):  
Time Series ◽  
Compressive Sensing ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action

Human action recognition via compressive-sensing-based dimensionality reduction

Optik ◽  
2015 ◽  
Vol 126 (9-10) ◽  
pp. 882-887 ◽  
Author(s):  
Jun Jiang ◽  
Xiaohai He ◽  
Mingliang Gao ◽  
Xiaofei Wang ◽  
Xiaoqiang Wu
Keyword(s):  
Dimensionality Reduction ◽  
Compressive Sensing ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action

scholarly journals Convolutional Long Short-Term Memory Hybrid Networks for Skeletal Based Human Action Recognition

2020 ◽  
Vol 9 (3) ◽  
pp. 955-961
Keyword(s):  
Time Series ◽  
Action Recognition ◽  
Short Term Memory ◽  
Human Action Recognition ◽  
Human Action ◽  
Short Term ◽  
Term Memory ◽  
Temporal Modeling ◽  
Long Short Term Memory ◽  
Spatio Temporal

The objective is to develop a time series image representation of the skeletal action data and use it for recognition through a convolutional long short-term deep learning framework. Consequently, Kinect captured human skeletal data is transformed into a Joint Change Distance Image (JCDI) descriptor which maps the time changes in the joints. Subsequently, JCDIs are decoded spatially well with a Convolutional (CNN). Temporal decomposition is executed on long short term memory (LSTM) with data changes along x , y and z position vectors of the skeleton. We propose a combination of CNN and LSTM which maps the spatio temporal information to generate a generalized time series features for recognition. Finally, scores are fused from spatially vibrant CNNs and temporally sound LSTMs for action recognition. Publicly available action datasets such as NTU RGBD, MSR Action, UTKinect and G3D were used as test inputs for experimentation. The results showed a better performance due to spatio temporal modeling at both the representation and the recognition stages when compared to other state-of-the-arts

scholarly journals CNN-based and DTW features for human activity recognition on depth maps

2021 ◽  
Author(s):  
Jacek Trelinski ◽  
Bogdan Kwolek
Keyword(s):  
Time Series ◽  
Action Recognition ◽  
Feature Vector ◽  
Multivariate Time Series ◽  
Depth Map ◽  
Human Action Recognition ◽  
Human Action ◽  
Training Time ◽  
Depth Maps ◽  
Class Labels

AbstractIn this work, we present a new algorithm for human action recognition on raw depth maps. At the beginning, for each class we train a separate one-against-all convolutional neural network (CNN) to extract class-specific features representing person shape. Each class-specific, multivariate time-series is processed by a Siamese multichannel 1D CNN or a multichannel 1D CNN to determine features representing actions. Afterwards, for the nonzero pixels representing the person shape in each depth map we calculate statistical features. On multivariate time-series of such features we determine Dynamic Time Warping (DTW) features. They are determined on the basis of DTW distances between all training time-series. Finally, each class-specific feature vector is concatenated with the DTW feature vector. For each action category we train a multiclass classifier, which predicts probability distribution of class labels. From pool of such classifiers we select a number of classifiers such that an ensemble built on them achieves the best classification accuracy. Action recognition is performed by a soft voting ensemble that averages distributions calculated by such classifiers with the largest discriminative power. We demonstrate experimentally that on MSR-Action3D and UTD-MHAD datasets the proposed algorithm attains promising results and outperforms several state-of-the-art depth-based algorithms.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Deep Learning for Human Action Recognition Survey

2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action

Human Action Recognition Based on Discriminative Sparse Coding Video Representation

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 745 ◽  
Author(s):  
Bin WANG ◽  
Yuanyuan WANG ◽  
Wenhua XIAO ◽  
Wei WANG ◽  
Maojun ZHANG
Keyword(s):  
Action Recognition ◽  
Sparse Coding ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Representation
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