Robust methods for hand gesture spotting and recognition using Hidden Markov Models and Conditional Random Fields

2010 ◽  
Author(s):  
Mahmoud Elmezain ◽  
Ayoub Al-Hamadi ◽  
Samy Sadek ◽  
Bernd Michaelis
Keyword(s):  
Hidden Markov Models ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Markov Models ◽  
Hidden Markov ◽  
Hand Gesture ◽  
Robust Methods ◽  
Gesture Spotting

AUV behavior recognition using behavior histograms, HMMs, and CRFs

Robotica ◽  
2014 ◽  
Vol 32 (2) ◽  
pp. 291-304 ◽  
Author(s):  
Michael Novitzky ◽  
Charles Pippin ◽  
Thomas R. Collins ◽  
Tucker R. Balch ◽  
Michael E. West
Keyword(s):  
Hidden Markov Models ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Markov Models ◽  
Hidden Markov ◽  
Task Assignment ◽  
Behavior Recognition ◽  
Sensor Information ◽  
Recognition Technique ◽  
And Task

SUMMARYThis paper focuses on behavior recognition in an underwater application as a substitute for communicating through acoustic transmissions, which can be unreliable. The importance of this work is that sensor information regarding other agents can be leveraged to perform behavior recognition, which is activity recognition of robots performing specific programmed behaviors, and task-assignment. This work illustrates the use of Behavior Histograms, Hidden Markov Models (HMMs), and Conditional Random Fields (CRFs) to perform behavior recognition. We present challenges associated with using each behavior recognition technique along with results on individually selected test trajectories, from simulated and real sonar data, and real-time recognition through a simulated mission.

scholarly journals Hidden-Markov-Models-Based Dynamic Hand Gesture Recognition

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaoyan Wang ◽  
Ming Xia ◽  
Huiwen Cai ◽  
Yong Gao ◽  
Carlo Cattani
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Hand Gesture ◽  
Recognition Method ◽  
Global Features ◽  
Adaboost Algorithm ◽  
Dynamic Hand Gesture Recognition ◽  
Hand Recognition ◽  
Curve Moments

This paper is concerned with the recognition of dynamic hand gestures. A method based on Hidden Markov Models (HMMs) is presented for dynamic gesture trajectory modeling and recognition. Adaboost algorithm is used to detect the user's hand and a contour-based hand tracker is formed combining condensation and partitioned sampling. Cubic B-spline is adopted to approximately fit the trajectory points into a curve. Invariant curve moments as global features and orientation as local features are computed to represent the trajectory of hand gesture. The proposed method can achieve automatic hand gesture online recognition and can successfully reject atypical gestures. The experimental results show that the proposed algorithm can reach better recognition results than the traditional hand recognition method.

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