Human behavior recognition based on 3D features and hidden markov models

2015 ◽  
Vol 10 (3) ◽  
pp. 495-502 ◽  
Author(s):  
Yuexin Wu ◽  
Zhe Jia ◽  
Yue Ming ◽  
Juanjuan Sun ◽  
Liujuan Cao
Keyword(s):  
Hidden Markov Models ◽  
Human Behavior ◽  
Markov Models ◽  
Hidden Markov ◽  
Behavior Recognition ◽  
Human Behavior Recognition

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.

Behavior Recognition and Prediction With Hidden Markov Models for Surveillance Systems

2009 ◽  
Vol 42 (3) ◽  
pp. 206-211
Author(s):  
Josef Mitterbauer ◽  
Dietmar Bruckner ◽  
Rosemarie Velik
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Surveillance Systems ◽  
Behavior Recognition

scholarly journals Context-Aware Mouse Behavior Recognition Using Hidden Markov Models

2019 ◽  
Vol 28 (3) ◽  
pp. 1133-1148 ◽  
Author(s):  
Zheheng Jiang ◽  
Danny Crookes ◽  
Brian D. Green ◽  
Yunfeng Zhao ◽  
Haiping Ma ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Context Aware ◽  
Mouse Behavior ◽  
Behavior Recognition

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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