Human Action Recognition Based on Self-Learning Feature and HMM

Authors

Jiarui Wen, Shanghai University, School of Mechatronic Engineering and Automation,Shanghai Key Laboratory of Power Station Automation, Shanghai 200072, China;
Lina Liu, Shanghai University, School of Mechatronic Engineering and Automation,Shanghai Key Laboratory of Power Station Automation, Shanghai 200072, China;
Rui Ling, Shanghai University, School of Mechatronic Engineering and Automation,Shanghai Key Laboratory of Power Station Automation, Shanghai 200072, China;
Shiwei Ma, Shanghai University, School of Mechatronic Engineering and Automation,Shanghai Key Laboratory of Power Station Automation, Shanghai 200072, China;

Abstract

Abstract: The current methods of human action recognition by computer vision are mostly based on hand-craft features and usually prior knowledge-required. They inevitably depend on specific applications and neglect the inner structure of visional information. A novel method which integrated self-learned pose features and combined posture symbol rules was proposed to achieve the recognition of human action more efficiently. The structural features of posture silhouette were extracted and a codebook of primary posture was built through the establishment of a sparse auto-encoder network. Then, in the phase of recognition, the Hidden Markov Model was employed to train the models for different action categories. Besides, a key frame extraction algorithm was developed to reduce the redundancy of long code sequence before training HMMs. Simulation experiments manifest the effectiveness of the proposed method.

Recommended Citation

Wen, Jiarui; Liu, Lina; Ling, Rui; and Ma, Shiwei (2020) "Human Action Recognition Based on Self-Learning Feature and HMM," Journal of System Simulation: Vol. 27: Iss. 8, Article 19.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss8/19

First Page

1782

Revised Date

2015-06-24

DOI Link

https://doi.org/

Last Page

1789

CLC

TP391.1

Recommended Citation

Wen Jiarui, Liu Lina, Rui Ling, Ma Shiwei. Human Action Recognition Based on Self-Learning Feature and HMM[J]. Journal of System Simulation, 2015, 27(8): 1782-1789.