Human Action Recognition Using Multilevel Depth Motion Maps
The advent of depth sensors opens up new opportunities for human action recognition by providing depth information. The main purpose of this paper is to present an effective method for human action recognition from depth images. A multilevel frame select sampling (MFSS) method are proposed to genera...
Main Authors: | Xu Weiyao, Wu Muqing, Zhao Min, Liu Yifeng, Lv Bo, Xia Ting |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8675733/ |
Similar Items
-
Multi-Temporal Depth Motion Maps-Based Local Binary Patterns for 3-D Human Action Recognition
by: Chen Chen, et al.
Published: (2017-01-01) -
Combining Adaptive Hierarchical Depth Motion Maps With Skeletal Joints for Human Action Recognition
by: Runwei Ding, et al.
Published: (2019-01-01) -
Combining adaptive hierarchical depth motion maps with skeletal joints for human action recognition
by: Ding, Runwei, et al.
Published: (2019) -
Exploring 3D Human Action Recognition Using STACOG on Multi-View Depth Motion Maps Sequences
by: Mohammad Farhad Bulbul, et al.
Published: (2021-05-01) -
Wearable Sensor-Based Human Activity Recognition via Two-Layer Diversity-Enhanced Multiclassifier Recognition Method
by: Yiming Tian, et al.
Published: (2019-04-01)