Paper Info
Title
Human action recognition based on scene semantics
Abstract
Like outdoors, indoor security is also a critical problem and human action recognition in indoor area is still a hot topic. Most studies on human action recognition ignored the semantic information of a scene, whereas indoors contains varieties of semantics. Meanwhile, the depth sensor with color and depth data is more suitable for extracting the semantics context in human actions. Hence, this paper proposed an indoor action recognition method using Kinect based on the semantics of a scene. First, we proposed a trajectory clustering algorithm for a three-dimensional (3D) scene by combining the different characteristics of people such as the spatial location, movement direction, and speed. Based on the clustering results and scene context, it concludes a region of interest (ROI) extraction method for indoors, and dynamic time warping (DTW) is used to study the abnormal action sequences. Finally, the color and depth-data-based 3D motion history image (3D–MHI) features and the semantics context of the scene were combined to recognize human action. In the experiment, two datasets were tested and the results demonstrate that our semantics-based method performs better than other methods.
Year
DOI
Venue
2019
10.1007/s11042-017-5496-x
Multimedia Tools and Applications
Keywords
Field
DocType
Action recognition, Depth sensor, Semantic context, Trajectory clustering
Computer vision,Pattern recognition,Dynamic time warping,Computer science,Action recognition,Trajectory clustering,Semantic information,Semantic context,Artificial intelligence,Region of interest,Cluster analysis,Semantics
Journal
Volume
Issue
ISSN
78.0
20
1573-7721
Citations 
PageRank 
References 
3
0.38
29
Authors
6
Name
Order
Citations
PageRank
Tao Hu D D S16924.10
Tao Hu D D S26924.10
Xinyan Zhu34414.27
Wei Guo4442146.38
shaohua wang5185.92
Jianfeng Zhu6217.02