Visual intent recognition in a multiple camera environment

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dc.contributor.advisor Savakis, Andreas - Chair en_US
dc.contributor.advisor Cockburn, Juan en_US
dc.contributor.advisor Hu, Fei en_US Erhard, Matthew en_US 2007-02-11T23:03:04Z en_US 2007-02-11T23:03:04Z en_US 2007-02-11T23:03:04Z en_US
dc.identifier.uri en_US
dc.description.abstract Activity recognition is an active field of research with many applications for both industrial and home use. Industry might use it as part of a security surveillance system, while home uses could be in applications such as smart rooms and aids for the disabled. This thesis develops one component of a “smart system” that can recognize certain activities related to the subject’s intent, i.e. where subjects concentrate their attention. A visual intent activity recognition system that operates in near real-time is created, based on multiple cameras. To accomplish this, a combination of face detection, facial feature detection, and pose estimation is used to estimate each subject’s gaze direction. To allow for better detection of the subject’s facial features, and thus more robust pose estimation, a multiple camera system is used. A wide-view camera is zoomed out and finds the subject, while a narrow-view camera zooms in to get more details on the face. Neural networks are then used to locate the mouth and eyes. A triangle template is matched to these features and used to estimate the subject’s pose in real-time. This method is used to determine where the subjects are looking and detect the activity of looking intently at a given location. A four-camera system recognizes the activity as occurring when at least one of two subjects is looking at the other. Testing showed that, on average, the pose estimate was accurate to within 5.08 degrees. The visual intent activity recognition system was able to correctly determine when one subject was looking at the other over 95% of the time. en_US
dc.language.iso en_US en_US
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dc.subject Activity recognition en_US
dc.subject Facial recognition en_US
dc.subject Feature detection en_US
dc.subject Pose estimation en_US
dc.subject Visual intent recognition en_US
dc.subject.lcc QP491 .E74 2006 en_US
dc.subject.lcsh Gaze--Data processing en_US
dc.subject.lcsh Eye--Movements--Data processing en_US
dc.subject.lcsh Computer vision en_US
dc.subject.lcsh Pattern recognition systems en_US
dc.subject.lcsh Human face recognition (Computer science) en_US
dc.subject.lcsh Neural networks (Computer science) en_US
dc.title Visual intent recognition in a multiple camera environment en_US
dc.type Thesis en_US
dc.description.defense 2006-12 en_US Kate Gleason College of Engineering en_US
dc.description.department Computer Engineering en_US
dc.description.approval 2006-12 en_US

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