Incremental EM for Probabilistic Latent Semantic Analysis on Human Action Recognition

2009 ◽  
Author(s):  
Jie Xu ◽  
Getian Ye ◽  
Yang Wang ◽  
Gunawan Herman ◽  
Bang Zhang ◽  
...  
Keyword(s):  
Action Recognition ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Human Action Recognition ◽  
Human Action ◽  
Probabilistic Latent Semantic Analysis

scholarly journals Adaptive Self-Occlusion Behavior Recognition Based on pLSA

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Hong-bin Tu ◽  
Li-min Xia ◽  
Lun-zheng Tan
Keyword(s):  
Random Field ◽  
Action Recognition ◽  
Markov Random Field ◽  
Semantic Analysis ◽  
Topic Model ◽  
Human Action Recognition ◽  
Human Action ◽  
Probabilistic Latent Semantic Analysis ◽  
Behavior Recognition ◽  
Markov Random

Human action recognition is an important area of human action recognition research. Focusing on the problem of self-occlusion in the field of human action recognition, a new adaptive occlusion state behavior recognition approach was presented based on Markov random field and probabilistic Latent Semantic Analysis (pLSA). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms an occlusion state variable by phase space obtained. Then, we proposed a hierarchical area variety model. Finally, we use the topic model of pLSA to recognize the human behavior. Experiments were performed on the KTH, Weizmann, and Humaneva dataset to test and evaluate the proposed method. The compared experiment results showed that what the proposed method can achieve was more effective than the compared methods.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Deep Learning for Human Action Recognition Survey

2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action

Human Action Recognition Based on Discriminative Sparse Coding Video Representation

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 745 ◽  
Author(s):  
Bin WANG ◽  
Yuanyuan WANG ◽  
Wenhua XIAO ◽  
Wei WANG ◽  
Maojun ZHANG
Keyword(s):  
Action Recognition ◽  
Sparse Coding ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Representation
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