A Computer Vision-Based Gesture Recognition Using Hidden Markov Model

Author(s):  
Keshav Sinha ◽  
Rashmi Kumari ◽  
Annu Priya ◽  
Partha Paul
Author(s):  
Meng Yu ◽  
Gang Chen ◽  
Zilong Huang ◽  
Qiang Wang ◽  
Yuan Chen

2014 ◽  
Author(s):  
Jing Jin ◽  
Yuanqing Wang ◽  
Liujing Xu ◽  
Liqun Cao ◽  
Lei Han ◽  
...  

2012 ◽  
Vol 433-440 ◽  
pp. 4651-4655
Author(s):  
Hui Qing Lu ◽  
Wei Nan Zhu ◽  
Yu Chao Zhou

This paper proposes an intelligent video analysis technology for elevator cage abnormality detection in computer vision. By collecting, processing, and analyzing video images in real time, the feature vectors including the variation of foreground pixels, the variation of length and width of foreground region’s enclosing rectangle and the variation of foreground region’s center of mass are obtained. The background is modeled by the Codebook Subtraction algorithm,these feature data are processed via K-Means clustering to get observation sequences, which are used to model a Hidden Markov Model (HMMs) for the normal activity. Last, the abnormalities are identified by the difference, which is predetermined by observing the normal and abnormal activity testing sequences, from normal activity model


Author(s):  
Kshitish Milind Deo ◽  
Tirtha Suresh Girolkar ◽  
Avanti Yashwant Kulkarni

2011 ◽  
Vol 40 (4) ◽  
pp. 495-516 ◽  
Author(s):  
Sara Bilal ◽  
Rini Akmeliawati ◽  
Amir A. Shafie ◽  
Momoh Jimoh E. Salami

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