ISBN: 9786139952670 - Fatigue Detection Based on Biosignals and Facial Expressions (6 results)

Paperback. Condition: Brand New. 52 pages. 8.66x5.91x0.12 inches. In Stock.

Taschenbuch. Condition: Neu. Fatigue Detection Based on Biosignals and Facial Expressions | Manjusha Deshmukh (u. a.) | Taschenbuch | 52 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139952670 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Inattentiveness in drivers is the major contributing factor in road crashes. Inattention can be caused by fatigue. Alertness of a person is typically characterized by various visual cues like eyelid movement, gaze movement, head movement and facial expression which can then be extracted and concluded into drowsiness level. Mental state of the driver can also be determined by the EEG signals. Thus this project focuses on simultaneously combining multiple visual and non-visual cues to yield a more robust fatigue characterization than a single input. This approach combines facial expressions like eyelid movement and yawning for fatigue detection. The facial features are detected and then the interest points are traced using Harris corner point detection. The area covered by these interest points determines the presence or absence of drowsiness. We are also using EEG signals to deduce the mental state of driver for detection of fatigue making the system more reliable. Hence the system effectively combines the various characteristics to help avoid the mishaps caused due to the presence of fatigue in the drivers. 52 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Inattentiveness in drivers is the major contributing factor in road crashes. Inattention can be caused by fatigue. Alertness of a person is typically characterized by various visual cues like eyelid movement, gaze movement, head movement and facial expression which can then be extracted and concluded into drowsiness level. Mental state of the driver can also be determined by the EEG signals. Thus this project focuses on simultaneously combining multiple visual and non-visual cues to yield a more robust fatigue characterization than a single input. This approach combines facial expressions like eyelid movement and yawning for fatigue detection. The facial features are detected and then the interest points are traced using Harris corner point detection. The area covered by these interest points determines the presence or absence of drowsiness. We are also using EEG signals to deduce the mental state of driver for detection of fatigue making the system more reliable. Hence the system effectively combines the various characteristics to help avoid the mishaps caused due to the presence of fatigue in the drivers.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Deshmukh ManjushaDedicated, resourceful education professional with proven ability to create and monitor policies and practices that promote a safe learning environment ensures and encourages continuous improvements for students and.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Inattentiveness in drivers is the major contributing factor in road crashes. Inattention can be caused by fatigue. Alertness of a person is typically characterized by various visual cues like eyelid movement, gaze movement, head movement and facial expression which can then be extracted and concluded into drowsiness level. Mental state of the driver can also be determined by the EEG signals. Thus this project focuses on simultaneously combining multiple visual and non-visual cues to yield a more robust fatigue characterization than a single input. This approach combines facial expressions like eyelid movement and yawning for fatigue detection. The facial features are detected and then the interest points are traced using Harris corner point detection. The area covered by these interest points determines the presence or absence of drowsiness. We are also using EEG signals to deduce the mental state of driver for detection of fatigue making the system more reliable. Hence the system effectively combines the various characteristics to help avoid the mishaps caused due to the presence of fatigue in the drivers.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch.…