scholarly journals Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition

2017 ◽  
Vol 47 (4) ◽  
pp. 617-627 ◽  
Author(s):  
Yanan Guo ◽  
Dapeng Tao ◽  
Weifeng Liu ◽  
Jun Cheng
Keyword(s):  
Action Recognition ◽  
Inertial Sensor ◽  
Human Action Recognition ◽  
Human Action ◽  
Cauchy Estimator

scholarly journals A Hierarchical Learning Approach for Human Action Recognition

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4946
Author(s):  
Nicolas Lemieux ◽  
Rita Noumeir
Keyword(s):  
Action Recognition ◽  
Inertial Sensor ◽  
Computational Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Practical Applications ◽  
Learning Framework ◽  
Sensor Signals ◽  
High Level ◽  
Computational Resources

In the domain of human action recognition, existing works mainly focus on using RGB, depth, skeleton and infrared data for analysis. While these methods have the benefit of being non-invasive, they can only be used within limited setups, are prone to issues such as occlusion and often need substantial computational resources. In this work, we address human action recognition through inertial sensor signals, which have a vast quantity of practical applications in fields such as sports analysis and human-machine interfaces. For that purpose, we propose a new learning framework built around a 1D-CNN architecture, which we validated by achieving very competitive results on the publicly available UTD-MHAD dataset. Moreover, the proposed method provides some answers to two of the greatest challenges currently faced by action recognition algorithms, which are (1) the recognition of high-level activities and (2) the reduction of their computational cost in order to make them accessible to embedded devices. Finally, this paper also investigates the tractability of the features throughout the proposed framework, both in time and duration, as we believe it could play an important role in future works in order to make the solution more intelligible, hardware-friendly and accurate.

A survey of depth and inertial sensor fusion for human action recognition

2015 ◽  
Vol 76 (3) ◽  
pp. 4405-4425 ◽  
Author(s):  
Chen Chen ◽  
Roozbeh Jafari ◽  
Nasser Kehtarnavaz
Keyword(s):  
Sensor Fusion ◽  
Action Recognition ◽  
Inertial Sensor ◽  
Human Action Recognition ◽  
Human Action

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
Sign in / Sign up

Export Citation Format

Share Document