A Global-Motion Analysis Method via Rough-Set-Based Video Pre-classification

Author(s):  
Zhe Yuan ◽  
Yu Wu ◽  
Guoyin Wang ◽  
Jianbo Li
Author(s):  
Manasi Pathade ◽  
Madhuri Khambete

Continuous monitoring and automatic detection of crowd activities is extremely helpful for management at public places to avoid any possible disaster. Analysis of crowded scene is a critical task as it typically involves poor resolution of objects, occlusions and complex dynamics. In this paper, we propose a novel, systematic and generalized method based on global motion analysis of people to detect Congestion situation in crowded scenes at entry/exit corridors. Our approach is tested on video footages acquired from surveillance cameras installed at exit corridors of public places. The results show the expediency of our approach.


Spine ◽  
2013 ◽  
Vol 38 (21) ◽  
pp. E1327-E1333 ◽  
Author(s):  
Michio Tojima ◽  
Naoshi Ogata ◽  
Arito Yozu ◽  
Masahiko Sumitani ◽  
Nobuhiko Haga

Spine ◽  
1997 ◽  
Vol 22 (17) ◽  
pp. 1996-2000 ◽  
Author(s):  
Tae-Hong Lim ◽  
Jason C. Eck ◽  
Howard S. An ◽  
Linda M. McGrady ◽  
Gerald F. Harris ◽  
...  

2012 ◽  
Vol 546-547 ◽  
pp. 1404-1408
Author(s):  
Ye Li ◽  
Bing Lu

Aiming at improving the disvantage of single attribute analysis in rough set, the problem of combined attributes analysis is researched in this paper. The single attribute ayalysis only consider the effect of the factor itself on the decison-making but neglect the interaction between multy-attributes influencing the results of the decision-making. So a new combined attributes weighting is presented in this paper.On this basis,a novel analysis method of combined attributes in rough set is proposed. The experiment and theory analysis shows that the new approch is feasible. In addition,it can mine the latent effect on decision making of combined attribute and make up for the shortage brought by single attribute analysis .


Author(s):  
Shusaku Nomura ◽  
◽  
Yasuo Kudo ◽  

This study aims at an application of rough set theory to illustrate the relationship between human psychological and physiological states. Recent behavioral medicine studies have revealed that various human secretory substances change according to mental states. These substances, the hormones and immune substances, show temporal increase against mental stress. Thus, it is frequently introduced as biomarkers of mental stress. The relationship between these biomarkers and human chronic stresses or daily mental states was also suggested in the previous studies. However the results of these studies were sometimes inconsistent with each other. Some technical reasons were indicated for this discrepancy. Among that, we focused on the analysis technique investigating the relationship between human psychological state, i.e., scores of a psychological scale, and physiological state, i.e., level of the secretory biomarkers. In this paper, we introduced Rough Set analysis method instead of using a conventional linear correlation analysis method. In the experiment, the salivary secretory immunoglobulin A (IgA), which is a major stress biomarker, of 20 male students was assessed before and after a short-term stressful mental workload. Also, 65 items of psychological mood scale was assessed as a psychological index. The result showed that some items strongly related with the change in the IgA, while no significant linear correlation was obtained among them.


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