FUSION OF DEPTH, SKELETON, AND INERTIAL DATA FOR HUMAN ACTION RECOGNITION Conference Paper

abstract

• 2016 IEEE. This paper presents a human action recognition approach by the simultaneous deployment of a second generation Kinect depth sensor and a wearable inertial sensor. Three data modalities consisting of depth images, skeleton joint positions, and inertial signals are fused by utilizing three collaborative representation classifiers. A database consisting of 10 actions performed by 6 subjects is put together to carry out two types of testing of the developed fusion approach: subject-generic and subject-specific. The overall recognition rates obtained from both types of testing indicate recognition improvements when fusing all the data modalities compared to the situations when data modalities are used individually.

name of conference

• 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

authors

• Jafari, Roozbeh

published proceedings

• 2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS

altmetric score

• 3

author list (cited authors)

• Chen, C., Jafari, R., & Kehtarnavazi, N.

citation count

• 58

complete list of authors

• Chen, Chen||Jafari, Roozbeh||Kehtarnavazi, Nasser

publication date

• March 2016

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing / sponsored by the Institute of Electrical and Electronics Engineers Signal Processing Society. ICASSP (Conference)  Journal

keywords

• Fusion Of Depth
• Human Action Recognition
• Second Generation Kinect Depth Sensor
• Skeleton And Inertial Data
• Wearable Inertial Sensor

Digital Object Identifier (DOI)

• 10.1109/icassp.2016.7472170

International Standard Book Number (ISBN) 13

• 9781479999880

start page

• 2712

end page

• 2716

volume

• 2016-May

URL

• http://dx.doi.org/10.1109/icassp.2016.7472170