Detecting Mutual Awareness Events

It is quite common that multiple human observers attend to a single static interest point. This is known as a mutual awareness event (MAWE). A preferred way to monitor these situations is with a camera that captures the human observers while using existing face detection and head pose estimation alg...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2012-12, Vol.34 (12), p.2327-2340
Main Authors: Cohen, M., Shimshoni, I., Rivlin, E., Adam, A.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Awareness - physiology
Bayes Theorem
Cameras
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Databases, Factual
Exact sciences and technology
Face - anatomy & histology
Face recognition
Head pose
Humans
Image Processing, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Magnetic heads
Mass Behavior
mutual awareness
Observers
Pattern recognition. Digital image processing. Computational geometry
Posture - physiology
Signal processing
Signal Processing, Computer-Assisted
Social Behavior
social signal processing
Software
sparse 3D structure
Three dimensional displays
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Summary:It is quite common that multiple human observers attend to a single static interest point. This is known as a mutual awareness event (MAWE). A preferred way to monitor these situations is with a camera that captures the human observers while using existing face detection and head pose estimation algorithms. The current work studies the underlying geometric constraints of MAWEs and reformulates them in terms of image measurements. The constraints are then used in a method that 1) detects whether such an interest point does exist, 2) determines where it is located, 3) identifies who was attending to it, and 4) reports where and when each observer was while attending to it. The method is also applied on another interesting event when a single moving human observer fixates on a single static interest point. The method can deal with the general case of an uncalibrated camera in a general environment. This is in contrast to other work on similar problems that inherently assumes a known environment or a calibrated camera. The method was tested on about 75 images from various scenes and robustly detects MAWEs and estimates their related attributes. Most of the images were found by searching the Internet.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2012.49