A Hidden Markov Model method for traffic incident detection using multiple features

2011 ◽  
Author(s):  
Yang Xu ◽  
Chengdong Wu ◽  
Jungang Zheng
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Incident Detection ◽  
Multiple Features ◽  
Traffic Incident ◽  
Model Method

scholarly journals A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guoliang Chen ◽  
Kaikai Ge
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Calculation Model ◽  
Opening Angle ◽  
Hand Gesture ◽  
Leap Motion ◽  
Recognition Method ◽  
Hand Gestures ◽  
Multiple Features

In this paper, a fusion method based on multiple features and hidden Markov model (HMM) is proposed for recognizing dynamic hand gestures corresponding to an operator’s instructions in robot teleoperation. In the first place, a valid dynamic hand gesture from continuously obtained data according to the velocity of the moving hand needs to be separated. Secondly, a feature set is introduced for dynamic hand gesture expression, which includes four sorts of features: palm posture, bending angle, the opening angle of the fingers, and gesture trajectory. Finally, HMM classifiers based on these features are built, and a weighted calculation model fusing the probabilities of four sorts of features is presented. The proposed method is evaluated by recognizing dynamic hand gestures acquired by leap motion (LM), and it reaches recognition rates of about 90.63% for LM-Gesture3D dataset created by the paper and 93.3% for Letter-gesture dataset, respectively.

scholarly journals A Proposed Hidden Markov Model Method for Dynamic Device Pairing on Internet of Things End-Devices

2021 ◽  
Vol 14 (3) ◽  
pp. 274-285
Author(s):  
Aji Gautama Putrada ◽  
Nur Ghaniaviyanto Ramadhan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Access Point ◽  
Received Signal Strength Indicator ◽  
Model Method ◽  
Iot Platform ◽  
High Level ◽  
Device Pairing ◽  
Better Than

Dynamic device pairing is a context-based zero-interaction method to pair end-devices in an IoT System based on Received Signal Strength Indicator (RSSI) values. But if RSSI detection is done in high level, the accuracy is troublesome due to poor sampling rates. This research proposes the Hidden Markov Model method to increase the performance of dynamic device pairing detection. This research implements an IoT system consisting an Access Point, an IoT End Device, an IoT Platform, and an IoT application and performs a comparison of two different methods to prove the concept. The results show that the precision of dynamic device pairing with HMM is better than without HMM and the value is 83,93%.

Multiple Sequence Alignment and Profile Analysis of Protein Family Utsing Hidden Markov Model

2012 ◽  
Vol 2 (6) ◽  
pp. 208-211
Author(s):  
Navjot Kaur ◽  
◽  
Rajbir Singh Cheema ◽  
Harmandeep Singh Harmandeep Singh
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Profile Analysis ◽  
Hidden Markov ◽  
Protein Family ◽  
Multiple Sequence

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

A Fast Voice Command Recognition Algorithm Based on the Hidden Markov Model Stationary Distribution

Vestnik MEI ◽  
2018 ◽  
Vol 5 (5) ◽  
pp. 65-72
Author(s):  
Pavel A. Paramonov ◽  
◽  
Ivan V. Ognev ◽  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Stationary Distribution ◽  
Hidden Markov ◽  
Recognition Algorithm ◽  
Voice Command
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