Human Driving Behavior Recognition Based on Hidden Markov Models

Author(s):  
Xiaoning Meng ◽  
Ka Keung Lee ◽  
Yangsheng Xu
Robotica ◽  
2014 ◽  
Vol 32 (2) ◽  
pp. 291-304 ◽  
Author(s):  
Michael Novitzky ◽  
Charles Pippin ◽  
Thomas R. Collins ◽  
Tucker R. Balch ◽  
Michael E. West

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.


Author(s):  
Naoki AKAI ◽  
Takatsugu HIRAYAMA ◽  
Luis Yoichi MORALES ◽  
Yasuhiro AKAGI ◽  
Hailong LIU ◽  
...  

2015 ◽  
Vol 10 (3) ◽  
pp. 495-502 ◽  
Author(s):  
Yuexin Wu ◽  
Zhe Jia ◽  
Yue Ming ◽  
Juanjuan Sun ◽  
Liujuan Cao

2019 ◽  
Vol 28 (3) ◽  
pp. 1133-1148 ◽  
Author(s):  
Zheheng Jiang ◽  
Danny Crookes ◽  
Brian D. Green ◽  
Yunfeng Zhao ◽  
Haiping Ma ◽  
...  

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

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