TY - GEN
T1 - Weighted Group Sparse Bayesian Learning for Human Activity Classification
AU - Fan, Yingxia
AU - Zhao, Juan
AU - Bai, Xia
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Recently, many researchers have focused on the human behavior recognition based on micro-Doppler signal. In this paper, we propose a sparse representation classification approach based on weighted group sparse Bayesian learning (SRC-WGSBL) for human activity classification, which introduces the property of group sparsity to distinguish the sparse coefficients between different classes. In addition, the use of Bayesian model for sparse coding is helpful to have robust classification performance in practice. Extensive experiments on a public database have been carried out to compare the performance of the proposed approach with support vector machine (SVM) and sparse representation classification based on orthogonal matching pursuit (SRC-OMP). Experimental results demonstrate that the proposed approach is effective and has better performance.
AB - Recently, many researchers have focused on the human behavior recognition based on micro-Doppler signal. In this paper, we propose a sparse representation classification approach based on weighted group sparse Bayesian learning (SRC-WGSBL) for human activity classification, which introduces the property of group sparsity to distinguish the sparse coefficients between different classes. In addition, the use of Bayesian model for sparse coding is helpful to have robust classification performance in practice. Extensive experiments on a public database have been carried out to compare the performance of the proposed approach with support vector machine (SVM) and sparse representation classification based on orthogonal matching pursuit (SRC-OMP). Experimental results demonstrate that the proposed approach is effective and has better performance.
KW - group sparsity
KW - human activity classification
KW - sparse Bayesian learning
KW - sparse representation
UR - http://www.scopus.com/inward/record.url?scp=85181055828&partnerID=8YFLogxK
U2 - 10.1109/Radar53847.2021.10028571
DO - 10.1109/Radar53847.2021.10028571
M3 - Conference contribution
AN - SCOPUS:85181055828
T3 - Proceedings of the IEEE Radar Conference
SP - 1550
EP - 1555
BT - 2021 CIE International Conference on Radar, Radar 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 CIE International Conference on Radar, Radar 2021
Y2 - 15 December 2021 through 19 December 2021
ER -